From 6dbd7097f68e6e30e7bdf8b76a5f188869007e73 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Wed, 13 May 2026 15:46:25 +0200 Subject: [PATCH 01/13] .gitignore add --- .gitignore | 148 +++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 145 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index 5b314e6..fabd4af 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,146 @@ -.history +# ============================================================================= +# macOS +# ============================================================================= +.DS_Store +.AppleDouble +.LSOverride +Icon +._* +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +# ============================================================================= +# Windows +# ============================================================================= +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db +*.stackdump +[Dd]esktop.ini +$RECYCLE.BIN/ +*.cab +*.msi +*.msix +*.msm +*.msp +*.lnk + +# ============================================================================= +# Python +# ============================================================================= +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST +*.manifest +*.spec +pip-log.txt +pip-delete-this-directory.txt +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ +*.mo +*.pot +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal +instance/ +.webassets-cache +.scrapy +docs/_build/ +.pybuilder/ +target/ +.ipynb_checkpoints +profile_default/ +ipython_config.py +__pypackages__/ +celerybeat-schedule +celerybeat.pid +*.sage.py +.env .venv -__pycache__ -*.pyc \ No newline at end of file +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +.spyderproject.db +.spyproject +.ropeproject +/site +.mypy_cache/ +.dmypy.json +dmypy.json +.pyre/ +.pytype/ +cython_debug/ + +# ============================================================================= +# JetBrains IDEs (IntelliJ, PyCharm, WebStorm, etc.) +# ============================================================================= +.idea/ +*.iws +*.iml +*.ipr +out/ +.idea_modules/ +atlassian-ide-plugin.xml +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# ============================================================================= +# Visual Studio Code +# ============================================================================= +.vscode/ +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json +!.vscode/*.code-snippets +.history/ +*.vsix + +# Project Specific +#todo From 6975a904138c0793786a143fc361202b15278e3d Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Wed, 13 May 2026 17:14:03 +0200 Subject: [PATCH 02/13] papers folder ignore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index fabd4af..c8f3755 100644 --- a/.gitignore +++ b/.gitignore @@ -143,4 +143,4 @@ fabric.properties *.vsix # Project Specific -#todo +papers/ From e7f1be6e429664e0746ed7d7ffe84245197ea5dc Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Wed, 13 May 2026 17:14:27 +0200 Subject: [PATCH 03/13] uv infrastracture --- .python-version | 1 + pyproject.toml | 4 + uv.lock | 957 ++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 962 insertions(+) create mode 100644 .python-version create mode 100644 uv.lock diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 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+[[package]] +name = "wta-optimization" +version = "0.1.0" +source = { editable = "." } +dependencies = [ + { name = "gurobipy" }, + { name = "matplotlib" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "pandas", version = "3.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "pulp" }, + { name = "seaborn" }, +] + +[package.metadata] +requires-dist = [ + { name = "gurobipy", specifier = ">=13.0.2" }, + { name = "matplotlib", specifier = ">=3.10.9" }, + { name = "numpy" }, + { name = "pandas", specifier = ">=2.3.3" }, + { name = "pulp" }, + { name = "seaborn", specifier = ">=0.13.2" }, +] From b1cdf4461d667dd6f3bcba7712ac53325b58bcf3 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Fri, 15 May 2026 11:45:27 +0200 Subject: [PATCH 04/13] gurobi andersen initial implement --- benchmark.py | 13 +++- src/wta_optimization/__init__.py | 5 +- src/wta_optimization/exact.py | 110 +++++++++++++++++++++++++++++++ 3 files changed, 125 insertions(+), 3 deletions(-) diff --git a/benchmark.py b/benchmark.py index 9dd15b5..99ae47c 100644 --- a/benchmark.py +++ b/benchmark.py @@ -7,7 +7,7 @@ import seaborn as sns from wta_optimization.data import generate_random_instance, load_instance_from_file -from wta_optimization.exact import solve_exact +from wta_optimization.exact import solve_exact, solve_branch_and_adjust from wta_optimization.heuristic import ( solve_greedy, solve_local_search, @@ -20,7 +20,8 @@ ("greedy", "Greedy", "tab:orange"), ("ls", "Greedy + Local Search", "tab:blue"), ("sa", "Simulated Annealing", "tab:green"), - ("exact", "Exact MIP", "black"), + ("exact", "Exact MIP (PuLP)", "black"), + ("bna", "Branch & Adjust (Gurobi)", "tab:red"), ] EXACT_TIME_LIMIT_SECONDS = 5400.0 @@ -46,11 +47,19 @@ def _evaluate_methods( warm_start=sol_greedy if use_exact_warm_start else None, time_limit_seconds=exact_time_limit_seconds, ) + + sol_bna = solve_branch_and_adjust( + instance, + warm_start=sol_greedy if use_exact_warm_start else None, + time_limit_seconds=exact_time_limit_seconds, + ) + return { "greedy": sol_greedy, "ls": sol_ls, "sa": sol_sa, "exact": sol_exact, + "bna": sol_bna, } diff --git a/src/wta_optimization/__init__.py b/src/wta_optimization/__init__.py index 686a8bf..89acba2 100644 --- a/src/wta_optimization/__init__.py +++ b/src/wta_optimization/__init__.py @@ -1,7 +1,7 @@ """Static Weapon Target Allocation optimization tools.""" from .data import generate_random_instance -from .exact import solve_exact +from .exact import solve_exact, solve_branch_and_adjust from .heuristic import solve_greedy, solve_local_search, solve_simulated_annealing from .models import WTASolution, WTAInstance @@ -13,4 +13,7 @@ "solve_greedy", "solve_local_search", "solve_simulated_annealing", + "solve_exact", + "solve_branch_and_adjust", + "solve_greedy", ] diff --git a/src/wta_optimization/exact.py b/src/wta_optimization/exact.py index 030c57a..cc54bb0 100644 --- a/src/wta_optimization/exact.py +++ b/src/wta_optimization/exact.py @@ -4,6 +4,8 @@ from time import perf_counter from typing import Sequence +import gurobipy as gp +from gurobipy import GRB import pulp from .models import WTAInstance, WTASolution, objective_value @@ -114,3 +116,111 @@ def solve_exact( method="exact_mip_pulp_linearized_warm_start" if warm_start_assignment is not None else "exact_mip_pulp_linearized", status=solver_status, ) + + +def solve_branch_and_adjust( + instance: WTAInstance, + warm_start: WTASolution | Sequence[Sequence[int]] | None = None, + time_limit_seconds: float = 5400.0, +) -> WTASolution: + """ + Solve the Static WTA problem using the Branch-and-Adjust algorithm. + Utilizes Gurobi's native piecewise-linear exponential constraints and a callback + to track the true non-linear objective. + """ + start = perf_counter() + warm_start_assignment = _normalize_warm_start(instance, warm_start) + + env = gp.Env(empty=True) + env.setParam("OutputFlag", 0) + env.start() + + model = gp.Model("WTA_Branch_and_Adjust", env=env) + model.setParam(GRB.Param.TimeLimit, time_limit_seconds) + model.setParam(GRB.Param.LazyConstraints, 1) + + weapons = instance.weapons + targets = instance.targets + + x = model.addVars(weapons, targets, vtype=GRB.BINARY, name="x") + y = model.addVars(targets, lb=-GRB.INFINITY, name="y") + z = model.addVars(targets, lb=0.0, name="z") + + if warm_start_assignment is not None: + for i in range(weapons): + for j in range(targets): + x[i, j].Start = warm_start_assignment[i][j] + + model.setObjective( + gp.quicksum(instance.target_values[j] * z[j] for j in range(targets)), + GRB.MINIMIZE + ) + + for i in range(weapons): + model.addConstr(gp.quicksum(x[i, j] for j in range(targets)) <= 1, name=f"w_{i}") + + EPSILON = 1e-9 + + for j in range(targets): + ln_q = [] + for i in range(weapons): + q_ij = 1.0 - instance.destruction_probabilities[i][j] + ln_q.append(math.log(max(q_ij, EPSILON))) + + model.addConstr( + y[j] == gp.quicksum(x[i, j] * ln_q[i] for i in range(weapons)), + name=f"y_def_{j}" + ) + + model.addGenConstrExp(y[j], z[j], name=f"exp_{j}") + + model._best_true_obj = float('inf') + model._best_assignment = None + + def branch_and_adjust_callback(cb_model, where): + if where == GRB.Callback.MIPSOL: + x_vals = cb_model.cbGetSolution(x) + + true_obj = 0.0 + for tj in range(targets): + surv = instance.target_values[tj] + for wi in range(weapons): + if x_vals[wi, tj] > 0.5: + surv *= (1.0 - instance.destruction_probabilities[wi][tj]) + true_obj += surv + + if true_obj < cb_model._best_true_obj: + cb_model._best_true_obj = true_obj + assignment_matrix = [[0 for _ in range(targets)] for _ in range(weapons)] + for wi in range(weapons): + for tj in range(targets): + if x_vals[wi, tj] > 0.5: + assignment_matrix[wi][tj] = 1 + cb_model._best_assignment = tuple(tuple(row) for row in assignment_matrix) + + + model.optimize(branch_and_adjust_callback) + + runtime = perf_counter() - start + + if model.Status == GRB.OPTIMAL: + status_str = "optimal" + elif model.Status == GRB.TIME_LIMIT: + status_str = "time_limit" + else: + status_str = f"status_{model.Status}" + + if model._best_assignment is not None: + final_assignment = model._best_assignment + final_obj = model._best_true_obj + else: + final_assignment = tuple(tuple(0 for _ in range(targets)) for _ in range(weapons)) + final_obj = float('inf') + + return WTASolution( + assignment=final_assignment, + objective_value=final_obj, + runtime_seconds=runtime, + method="branch_and_adjust_gurobi", + status=status_str, + ) \ No newline at end of file From 4f80e4ebbdeb6e419bc07c48cce4e5272ef11c50 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Sun, 17 May 2026 19:21:26 +0200 Subject: [PATCH 05/13] gurobi remove andresen implement --- benchmark.py | 2 +- pyproject.toml | 2 +- src/wta_optimization/exact.py | 153 +++++++++++++++++++--------------- uv.lock | 77 +++++++++-------- 4 files changed, 133 insertions(+), 101 deletions(-) diff --git a/benchmark.py b/benchmark.py index 99ae47c..769b216 100644 --- a/benchmark.py +++ b/benchmark.py @@ -21,7 +21,7 @@ ("ls", "Greedy + Local Search", "tab:blue"), ("sa", "Simulated Annealing", "tab:green"), ("exact", "Exact MIP (PuLP)", "black"), - ("bna", "Branch & Adjust (Gurobi)", "tab:red"), + ("bna", "Branch & Adjust (SCIP)", "tab:red"), ] EXACT_TIME_LIMIT_SECONDS = 5400.0 diff --git a/pyproject.toml b/pyproject.toml index 8b05ac2..799c8c6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ dependencies = [ "pandas>=2.3.3", "matplotlib>=3.10.9", "seaborn>=0.13.2", - "gurobipy>=13.0.2", + "pyscipopt>=6.2.1", ] [tool.setuptools] diff --git a/src/wta_optimization/exact.py b/src/wta_optimization/exact.py index cc54bb0..fe00984 100644 --- a/src/wta_optimization/exact.py +++ b/src/wta_optimization/exact.py @@ -4,9 +4,8 @@ from time import perf_counter from typing import Sequence -import gurobipy as gp -from gurobipy import GRB import pulp +from pyscipopt import Model, Eventhdlr, SCIP_EVENTTYPE, quicksum from .models import WTAInstance, WTASolution, objective_value @@ -39,7 +38,7 @@ def solve_exact( ) -> WTASolution: """ Solve the Static WTA problem using an exact MILP formulation via pulp. - Uses log transformation mapping and piecewise linear approximation for the + Uses log transformation mapping and piecewise linear approximation for the exponential objective reduction. """ start = perf_counter() @@ -48,7 +47,7 @@ def solve_exact( prob = pulp.LpProblem("Static_WTA_MIP", pulp.LpMinimize) # Decision variables - x = [[pulp.LpVariable(f"x_{i}_{j}", cat=pulp.LpBinary) + x = [[pulp.LpVariable(f"x_{i}_{j}", cat=pulp.LpBinary) for j in range(instance.targets)] for i in range(instance.weapons)] z = [pulp.LpVariable(f"z_{j}", lowBound=0.0) for j in range(instance.targets)] @@ -107,7 +106,7 @@ def solve_exact( assignment[i][j] = 1 frozen_assignment = tuple(tuple(row) for row in assignment) - + runtime = perf_counter() - start return WTASolution( assignment=frozen_assignment, @@ -118,46 +117,77 @@ def solve_exact( ) +class TrueObjectiveTracker(Eventhdlr): + """ + SCIP Event Handler that tracks the true nonlinear objective + whenever a new integer-feasible solution is found in the tree. + """ + + def __init__(self, instance: WTAInstance, x_vars: list[list]): + self.instance = instance + self.x_vars = x_vars + self.best_true_obj = float('inf') + self.best_assignment = None + + def eventinit(self): + self.model.catchEvent(SCIP_EVENTTYPE.BESTSOLFOUND, self) + + def eventexit(self): + self.model.dropEvent(SCIP_EVENTTYPE.BESTSOLFOUND, self) + + def eventexec(self, event): + sol = self.model.getBestSol() + if sol is None: + return + + true_obj = 0.0 + targets = self.instance.targets + weapons = self.instance.weapons + assignment = [[0 for _ in range(targets)] for _ in range(weapons)] + + for j in range(targets): + surv = self.instance.target_values[j] + for i in range(weapons): + val = self.model.getSolVal(sol, self.x_vars[i][j]) + if val > 0.5: + surv *= (1.0 - self.instance.destruction_probabilities[i][j]) + assignment[i][j] = 1 + true_obj += surv + + if true_obj < self.best_true_obj: + self.best_true_obj = true_obj + self.best_assignment = tuple(tuple(row) for row in assignment) + + def solve_branch_and_adjust( instance: WTAInstance, + num_piecewise_segments: int = 20, warm_start: WTASolution | Sequence[Sequence[int]] | None = None, time_limit_seconds: float = 5400.0, ) -> WTASolution: """ - Solve the Static WTA problem using the Branch-and-Adjust algorithm. - Utilizes Gurobi's native piecewise-linear exponential constraints and a callback - to track the true non-linear objective. + Solve the Static WTA problem using the Branch-and-Adjust algorithm via SCIP. + Utilizes PySCIPOpt, piecewise-linear convex under-approximation, and an Event + Handler to track the true non-linear objective. """ start = perf_counter() warm_start_assignment = _normalize_warm_start(instance, warm_start) - env = gp.Env(empty=True) - env.setParam("OutputFlag", 0) - env.start() - - model = gp.Model("WTA_Branch_and_Adjust", env=env) - model.setParam(GRB.Param.TimeLimit, time_limit_seconds) - model.setParam(GRB.Param.LazyConstraints, 1) + model = Model("WTA_Branch_and_Adjust_SCIP") + model.hideOutput(True) + model.setRealParam("limits/time", time_limit_seconds) weapons = instance.weapons targets = instance.targets - x = model.addVars(weapons, targets, vtype=GRB.BINARY, name="x") - y = model.addVars(targets, lb=-GRB.INFINITY, name="y") - z = model.addVars(targets, lb=0.0, name="z") + x = [[model.addVar(vtype="B", name=f"x_{i}_{j}") for j in range(targets)] for i in range(weapons)] + y = [model.addVar(vtype="C", lb=None, name=f"y_{j}") for j in range(targets)] + z = [model.addVar(vtype="C", lb=0.0, name=f"z_{j}") for j in range(targets)] - if warm_start_assignment is not None: - for i in range(weapons): - for j in range(targets): - x[i, j].Start = warm_start_assignment[i][j] - - model.setObjective( - gp.quicksum(instance.target_values[j] * z[j] for j in range(targets)), - GRB.MINIMIZE - ) + model.setObjective(quicksum(instance.target_values[j] * z[j] for j in range(targets)), "minimize") for i in range(weapons): - model.addConstr(gp.quicksum(x[i, j] for j in range(targets)) <= 1, name=f"w_{i}") + model.addCons(quicksum(x[i][j] for j in range(targets)) <= 1, name=f"w_{i}") EPSILON = 1e-9 @@ -167,52 +197,43 @@ def solve_branch_and_adjust( q_ij = 1.0 - instance.destruction_probabilities[i][j] ln_q.append(math.log(max(q_ij, EPSILON))) - model.addConstr( - y[j] == gp.quicksum(x[i, j] * ln_q[i] for i in range(weapons)), - name=f"y_def_{j}" - ) - - model.addGenConstrExp(y[j], z[j], name=f"exp_{j}") - - model._best_true_obj = float('inf') - model._best_assignment = None + model.addCons(y[j] == quicksum(x[i][j] * ln_q[i] for i in range(weapons)), name=f"y_def_{j}") - def branch_and_adjust_callback(cb_model, where): - if where == GRB.Callback.MIPSOL: - x_vals = cb_model.cbGetSolution(x) + min_y_j = sum(val for val in ln_q if val < 0) - true_obj = 0.0 - for tj in range(targets): - surv = instance.target_values[tj] - for wi in range(weapons): - if x_vals[wi, tj] > 0.5: - surv *= (1.0 - instance.destruction_probabilities[wi][tj]) - true_obj += surv + if min_y_j < 0: + step = abs(min_y_j) / max(1, num_piecewise_segments - 1) + for k in range(num_piecewise_segments): + pk = min_y_j + k * step + exp_pk = math.exp(pk) + model.addCons(z[j] >= exp_pk + exp_pk * (y[j] - pk)) + else: + model.addCons(z[j] >= 1.0) - if true_obj < cb_model._best_true_obj: - cb_model._best_true_obj = true_obj - assignment_matrix = [[0 for _ in range(targets)] for _ in range(weapons)] - for wi in range(weapons): - for tj in range(targets): - if x_vals[wi, tj] > 0.5: - assignment_matrix[wi][tj] = 1 - cb_model._best_assignment = tuple(tuple(row) for row in assignment_matrix) + if warm_start_assignment is not None: + sol = model.createSol() + for i in range(weapons): + for j in range(targets): + model.setSolVal(sol, x[i][j], warm_start_assignment[i][j]) + model.addSol(sol) + tracker = TrueObjectiveTracker(instance, x) + model.includeEventhdlr(tracker, "TrueObjTracker", "Tracks true objective on new best solutions") - model.optimize(branch_and_adjust_callback) + model.optimize() runtime = perf_counter() - start - if model.Status == GRB.OPTIMAL: - status_str = "optimal" - elif model.Status == GRB.TIME_LIMIT: - status_str = "time_limit" - else: - status_str = f"status_{model.Status}" + scip_status = model.getStatus() + status_map = { + "optimal": "optimal", + "timelimit": "time_limit", + } + status_str = status_map.get(scip_status, f"status_{scip_status}") - if model._best_assignment is not None: - final_assignment = model._best_assignment - final_obj = model._best_true_obj + if tracker.best_assignment is not None: + final_assignment = tracker.best_assignment + 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= "python-dateutil" version = "2.9.0.post0" @@ -936,22 +947,22 @@ name = "wta-optimization" version = "0.1.0" source = { editable = "." } dependencies = [ - { name = "gurobipy" }, { name = "matplotlib" }, { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, { name = "pandas", version = "3.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pulp" }, + { name = "pyscipopt" }, { name = "seaborn" }, ] [package.metadata] requires-dist = [ - { name = "gurobipy", specifier = ">=13.0.2" }, { name = "matplotlib", specifier = ">=3.10.9" }, { name = "numpy" }, { name = "pandas", specifier = ">=2.3.3" }, { name = "pulp" }, + { name = "pyscipopt", specifier = ">=6.2.1" }, { name = "seaborn", specifier = ">=0.13.2" }, ] From b0719d10415fa8e28095d1ef0b307a0c296346b0 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Wed, 20 May 2026 21:45:31 +0200 Subject: [PATCH 06/13] gurobi solver switch --- pyproject.toml | 1 + src/wta_optimization/exact.py | 262 +++++++++++++++++++--------------- uv.lock | 33 +++++ 3 files changed, 183 insertions(+), 113 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 799c8c6..0b759f2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ dependencies = [ "matplotlib>=3.10.9", "seaborn>=0.13.2", "pyscipopt>=6.2.1", + "gurobipy>=13.0.2", ] [tool.setuptools] diff --git a/src/wta_optimization/exact.py b/src/wta_optimization/exact.py index fe00984..a4c2fde 100644 --- a/src/wta_optimization/exact.py +++ b/src/wta_optimization/exact.py @@ -5,7 +5,8 @@ from typing import Sequence import pulp -from pyscipopt import Model, Eventhdlr, SCIP_EVENTTYPE, quicksum +import gurobipy as gp +from gurobipy import GRB from .models import WTAInstance, WTASolution, objective_value @@ -49,7 +50,7 @@ def solve_exact( # Decision variables x = [[pulp.LpVariable(f"x_{i}_{j}", cat=pulp.LpBinary) for j in range(instance.targets)] for i in range(instance.weapons)] - + z = [pulp.LpVariable(f"z_{j}", lowBound=0.0) for j in range(instance.targets)] y = [pulp.LpVariable(f"y_{j}") for j in range(instance.targets)] @@ -73,13 +74,13 @@ def solve_exact( q_ij = 1.0 - instance.destruction_probabilities[i][j] q_ij = max(q_ij, EPSILON) ln_q.append(math.log(q_ij)) - + # y_j = sum_i( x_{ij} * ln(q_ij) ) prob += y[j] == pulp.lpSum([x[i][j] * ln_q[i] for i in range(instance.weapons)]) - + # Piecewise linear envelope for exp(y_j) (since exp is convex, max of tangents is a lower bound) min_y_j = sum(val for val in ln_q if val < 0) - + if min_y_j < 0: step = abs(min_y_j) / max(1, num_piecewise_segments - 1) for k in range(num_piecewise_segments): @@ -117,48 +118,6 @@ def solve_exact( ) -class TrueObjectiveTracker(Eventhdlr): - """ - SCIP Event Handler that tracks the true nonlinear objective - whenever a new integer-feasible solution is found in the tree. - """ - - def __init__(self, instance: WTAInstance, x_vars: list[list]): - self.instance = instance - self.x_vars = x_vars - self.best_true_obj = float('inf') - self.best_assignment = None - - def eventinit(self): - self.model.catchEvent(SCIP_EVENTTYPE.BESTSOLFOUND, self) - - def eventexit(self): - self.model.dropEvent(SCIP_EVENTTYPE.BESTSOLFOUND, self) - - def eventexec(self, event): - sol = self.model.getBestSol() - if sol is None: - return - - true_obj = 0.0 - targets = self.instance.targets - weapons = self.instance.weapons - assignment = [[0 for _ in range(targets)] for _ in range(weapons)] - - for j in range(targets): - surv = self.instance.target_values[j] - for i in range(weapons): - val = self.model.getSolVal(sol, self.x_vars[i][j]) - if val > 0.5: - surv *= (1.0 - self.instance.destruction_probabilities[i][j]) - assignment[i][j] = 1 - true_obj += surv - - if true_obj < self.best_true_obj: - self.best_true_obj = true_obj - self.best_assignment = tuple(tuple(row) for row in assignment) - - def solve_branch_and_adjust( instance: WTAInstance, num_piecewise_segments: int = 20, @@ -166,82 +125,159 @@ def solve_branch_and_adjust( time_limit_seconds: float = 5400.0, ) -> WTASolution: """ - Solve the Static WTA problem using the Branch-and-Adjust algorithm via SCIP. - Utilizes PySCIPOpt, piecewise-linear convex under-approximation, and an Event - Handler to track the true non-linear objective. + Solve the Static WTA problem using the true Branch-and-Adjust algorithm via Gurobi. + Uses continuous Piecewise Linear Under-Approximations and Lazy Constraints to + dynamically "Adjust" the node objectives when integer solutions are found. """ start = perf_counter() warm_start_assignment = _normalize_warm_start(instance, warm_start) - model = Model("WTA_Branch_and_Adjust_SCIP") - model.hideOutput(True) - model.setRealParam("limits/time", time_limit_seconds) - - weapons = instance.weapons - targets = instance.targets - - x = [[model.addVar(vtype="B", name=f"x_{i}_{j}") for j in range(targets)] for i in range(weapons)] - y = [model.addVar(vtype="C", lb=None, name=f"y_{j}") for j in range(targets)] - z = [model.addVar(vtype="C", lb=0.0, name=f"z_{j}") for j in range(targets)] - - model.setObjective(quicksum(instance.target_values[j] * z[j] for j in range(targets)), "minimize") - - for i in range(weapons): - model.addCons(quicksum(x[i][j] for j in range(targets)) <= 1, name=f"w_{i}") + # Używamy context managerów Gurobi (dobre praktyki zarządzania licencją) + with gp.Env(empty=True) as env: + env.setParam('OutputFlag', 0) # Ustaw na 1, jeśli chcesz logi Gurobi w konsoli + env.start() - EPSILON = 1e-9 + with gp.Model("WTA_Branch_and_Adjust_Gurobi", env=env) as model: + # Włączamy użycie Lazy Constraints w Gurobi! (KRYTYCZNE) + model.Params.LazyConstraints = 1 + if time_limit_seconds is not None: + model.Params.TimeLimit = time_limit_seconds - for j in range(targets): - ln_q = [] - for i in range(weapons): - q_ij = 1.0 - instance.destruction_probabilities[i][j] - ln_q.append(math.log(max(q_ij, EPSILON))) - - model.addCons(y[j] == quicksum(x[i][j] * ln_q[i] for i in range(weapons)), name=f"y_def_{j}") - - min_y_j = sum(val for val in ln_q if val < 0) + weapons = instance.weapons + targets = instance.targets - if min_y_j < 0: - step = abs(min_y_j) / max(1, num_piecewise_segments - 1) - for k in range(num_piecewise_segments): - pk = min_y_j + k * step - exp_pk = math.exp(pk) - model.addCons(z[j] >= exp_pk + exp_pk * (y[j] - pk)) - else: - model.addCons(z[j] >= 1.0) + # Tworzenie zmiennych (używamy słowników dla łatwego dostępu w Gurobi) + x = {} + for i in range(weapons): + for j in range(targets): + x[i, j] = model.addVar(vtype=GRB.BINARY, name=f"x_{i}_{j}") - if warm_start_assignment is not None: - sol = model.createSol() - for i in range(weapons): + y = {} + z = {} for j in range(targets): - model.setSolVal(sol, x[i][j], warm_start_assignment[i][j]) - model.addSol(sol) - - tracker = TrueObjectiveTracker(instance, x) - model.includeEventhdlr(tracker, "TrueObjTracker", "Tracks true objective on new best solutions") - - model.optimize() + y[j] = model.addVar(lb=-GRB.INFINITY, name=f"y_{j}") + z[j] = model.addVar(lb=0.0, name=f"z_{j}") - runtime = perf_counter() - start + # Cel - minimalizacja przybliżonych kosztów Z + model.setObjective(gp.quicksum(instance.target_values[j] * z[j] for j in range(targets)), GRB.MINIMIZE) - scip_status = model.getStatus() - status_map = { - "optimal": "optimal", - "timelimit": "time_limit", - } - status_str = status_map.get(scip_status, f"status_{scip_status}") + # Każda broń użyta max raz + for i in range(weapons): + model.addConstr(gp.quicksum(x[i, j] for j in range(targets)) <= 1, name=f"w_{i}") - if tracker.best_assignment is not None: - final_assignment = tracker.best_assignment - final_obj = tracker.best_true_obj - else: - final_assignment = tuple(tuple(0 for _ in range(targets)) for _ in range(weapons)) - final_obj = float('inf') + EPSILON = 1e-9 - return WTASolution( - assignment=final_assignment, - objective_value=final_obj, - runtime_seconds=runtime, - method="branch_and_adjust_scip", - status=status_str, - ) \ No newline at end of file + # Przybliżenie logarytmiczne i styczne pod krzywą (WTA_LA z artykułu) + for j in range(targets): + ln_q = [] + for i in range(weapons): + q_ij = 1.0 - instance.destruction_probabilities[i][j] + ln_q.append(math.log(max(q_ij, EPSILON))) + + model.addConstr(y[j] == gp.quicksum(x[i, j] * ln_q[i] for i in range(weapons)), name=f"y_def_{j}") + + min_y_j = sum(val for val in ln_q if val < 0) + + if min_y_j < 0: + step = abs(min_y_j) / max(1, num_piecewise_segments - 1) + for k in range(num_piecewise_segments): + pk = min_y_j + k * step + exp_pk = math.exp(pk) + model.addConstr(z[j] >= exp_pk + exp_pk * (y[j] - pk)) + else: + model.addConstr(z[j] >= 1.0) + + # Inicjacja rozwiązania (Warm Start) + if warm_start_assignment is not None: + for i in range(weapons): + for j in range(targets): + x[i, j].Start = warm_start_assignment[i][j] + + # Rejestrujemy obiekty w modelu, żeby Callback miał do nich dostęp + model._x = x + model._z = z + model._instance = instance + model._best_true_obj = float('inf') + model._best_assignment = None + + # --------------------------------------------------------- + # MAGIA BRANCH-AND-ADJUST: GUROBI LAZY CALLBACK + # --------------------------------------------------------- + def bna_callback(cb_model, where): + # Gdy solver zatrzyma się na pełnym węźle całkowitoliczbowym + if where == GRB.Callback.MIPSOL: + # Pobieramy to próbne przypisanie i jego przybliżone koszty + x_val = cb_model.cbGetSolution(cb_model._x) + z_val = cb_model.cbGetSolution(cb_model._z) + + targets = cb_model._instance.targets + weapons = cb_model._instance.weapons + + true_z = [1.0] * targets + S1_vars = [] + S0_vars = [] + + # Liczymy prawdziwy nieliniowy wzór ułożenia + for i in range(weapons): + for j in range(targets): + if x_val[i, j] > 0.5: + true_z[j] *= (1.0 - cb_model._instance.destruction_probabilities[i][j]) + S1_vars.append(cb_model._x[i, j]) + else: + S0_vars.append(cb_model._x[i, j]) + + true_obj = sum(cb_model._instance.target_values[j] * true_z[j] for j in range(targets)) + + # Notujemy sobie ten układ w razie czego (gdyby limit czasu zablokował solver) + if true_obj < cb_model._best_true_obj: + cb_model._best_true_obj = true_obj + assignment = [[0 for _ in range(targets)] for _ in range(weapons)] + for i in range(weapons): + for j in range(targets): + if x_val[i, j] > 0.5: + assignment[i][j] = 1 + cb_model._best_assignment = tuple(tuple(row) for row in assignment) + + # KROK ADJUST: Sprawdzamy czy przybliżony model zaniżył koszty. + # Zamiast 'reject()', w Gurobi wysyłamy poprawkę kosztu dla TEGO KONKRETNEGO ułożenia. + delta_x = None # Wskaźnik, który wynosi 0 TYLKO wtedy, gdy ułożenie to dokładnie to obecne + + for j in range(targets): + if z_val[j] < true_z[j] - 1e-6: # Model skłamał (zaniżył) o więcej niż EPSILON + if delta_x is None: + # Konstrukcja odległości Hamminga dla macierzy binarnej w Gurobi + delta_x = len(S1_vars) - gp.quicksum(S1_vars) + gp.quicksum(S0_vars) + + # Dodajemy tzw. cięcie no-good cut. + # Jeśli badane ułożenie jest dokładnie tym, które znaleźliśmy (delta_x == 0), + # to wymuszamy matematycznie: Z musi podskoczyć do Prawdziwego Z. + # Dla innych ułożeń (delta >= 1), równanie się kasuje do 0 i nie przeszkadza drzewu. + cb_model.cbLazy(cb_model._z[j] >= true_z[j] * (1.0 - delta_x)) + + # Start optymalizacji wraz z "pilnującym" go callbackiem + model.optimize(bna_callback) + + runtime = perf_counter() - start + + status = model.Status + status_map = { + GRB.OPTIMAL: "optimal", + GRB.TIME_LIMIT: "time_limit", + GRB.INFEASIBLE: "infeasible", + } + status_str = status_map.get(status, 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}, + { name = "scipy", specifier = ">=1.15.3" }, { name = "seaborn", specifier = ">=0.13.2" }, ] From 49013fc945bf9b6c376359f062ac91889a84b912 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Thu, 21 May 2026 17:32:58 +0200 Subject: [PATCH 08/13] add black and isort automatic --- .github/workflows/format-and-sort.yml | 42 +++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 .github/workflows/format-and-sort.yml diff --git a/.github/workflows/format-and-sort.yml b/.github/workflows/format-and-sort.yml new file mode 100644 index 0000000..1aeb154 --- /dev/null +++ b/.github/workflows/format-and-sort.yml @@ -0,0 +1,42 @@ +name: Format and Sort Imports + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + format: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 # Pobiera pełną historię, potrzebną do commitowania + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.12" + + - name: Install Black and isort + run: | + python -m pip install --upgrade pip + pip install black isort + + - name: Format code with Black + run: black . + + - name: Sort imports with isort + run: isort . + + - name: Commit changes + run: | + git config --global user.name "GitHub Actions" + git config --global user.email "actions@github.com" + git add . + # Sprawdź, czy są zmiany do commita + if ! git diff --cached --quiet; then + git commit -m "Auto-format code and sort imports [skip ci]" + git push + fi \ No newline at end of file From 189c53c7fa2c976e9b2acafb22bdc4e3ac7f1c3b Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Thu, 21 May 2026 17:35:28 +0200 Subject: [PATCH 09/13] github action quick fix push --- .github/workflows/format-and-sort.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/format-and-sort.yml b/.github/workflows/format-and-sort.yml index 1aeb154..26f31be 100644 --- a/.github/workflows/format-and-sort.yml +++ b/.github/workflows/format-and-sort.yml @@ -12,7 +12,8 @@ jobs: steps: - uses: actions/checkout@v4 with: - fetch-depth: 0 # Pobiera pełną historię, potrzebną do commitowania + fetch-depth: 0 # Pobiera pełną historię + ref: ${{ github.head_ref || github.ref_name }} # Obsługuje zarówno PR, jak i push - name: Set up Python uses: actions/setup-python@v4 @@ -35,8 +36,7 @@ jobs: git config --global user.name "GitHub Actions" git config --global user.email "actions@github.com" git add . - # Sprawdź, czy są zmiany do commita if ! git diff --cached --quiet; then git commit -m "Auto-format code and sort imports [skip ci]" - git push + git push origin HEAD:${{ github.ref_name }} fi \ No newline at end of file From 8dfc3e489d2494f650cec1f6a0effab3d25e4d15 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Thu, 21 May 2026 17:44:17 +0200 Subject: [PATCH 10/13] github action quick fix push2 --- .github/workflows/format-and-sort.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/format-and-sort.yml b/.github/workflows/format-and-sort.yml index 26f31be..1d36e80 100644 --- a/.github/workflows/format-and-sort.yml +++ b/.github/workflows/format-and-sort.yml @@ -38,5 +38,5 @@ jobs: git add . if ! git diff --cached --quiet; then git commit -m "Auto-format code and sort imports [skip ci]" - git push origin HEAD:${{ github.ref_name }} + git push origin HEAD:${{ github.head_ref || github.ref_name }} fi \ No newline at end of file From 0d77fb2e54f6fb59670d6e308687719aaab39248 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Fri, 22 May 2026 15:01:25 +0200 Subject: [PATCH 11/13] branch and adjust fix reject step --- src/wta_optimization/exact.py | 481 ++++++++++++++++++++++++---------- 1 file changed, 336 insertions(+), 145 deletions(-) diff --git a/src/wta_optimization/exact.py b/src/wta_optimization/exact.py index a4c2fde..e6f03ef 100644 --- a/src/wta_optimization/exact.py +++ b/src/wta_optimization/exact.py @@ -7,14 +7,26 @@ import pulp import gurobipy as gp from gurobipy import GRB +from scipy.optimize import broyden1 from .models import WTAInstance, WTASolution, objective_value +# --------------------------------------------------------------------------- +# Numeric constants +# --------------------------------------------------------------------------- +_EPS = 1e-10 +_MIN_EXP = 1e-320 + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- def _normalize_warm_start( - instance: WTAInstance, - warm_start: WTASolution | Sequence[Sequence[int]] | None, + instance: WTAInstance, + warm_start: WTASolution | Sequence[Sequence[int]] | None, ) -> tuple[tuple[int, ...], ...] | None: + """Normalizes the warm start to a tuple-of-tuples with binary values.""" if warm_start is None: return None @@ -22,35 +34,117 @@ def _normalize_warm_start( if len(assignment) != instance.weapons: raise ValueError("warm start assignment row count must match the number of weapons") - normalized_assignment: list[tuple[int, ...]] = [] + normalized: list[tuple[int, ...]] = [] for row in assignment: if len(row) != instance.targets: - raise ValueError("each warm start assignment row must match the number of targets") - normalized_assignment.append(tuple(int(bool(value)) for value in row)) + raise ValueError("each warm start row must match the number of targets") + normalized.append(tuple(int(bool(v)) for v in row)) + + return tuple(normalized) + + +def _compute_breakpoints( + destruction_probs: tuple[tuple[float, ...], ...], + mu: list[int], + j: int, + delta: float, +) -> list[float]: + """ + Iterative delta-based procedure from Section 5 of Andersen (equations 5.2-5.4). + + Returns a list of breakpoints b[j,0], b[j,1], ..., b[j,T] = 0 + guaranteeing a maximum approximation error of exactly delta. + + Parameters + ---------- + destruction_probs : matrix p[i][j] + mu : list mu[i] — availability of each weapon type + j : target index + delta : maximum allowed approximation error + """ + weapons = len(destruction_probs) + + # Equation (3.14): smallest breakpoint = ln(product of (1-p_ij)^mu_i for i) + # Corresponds to the scenario "all weapons fired at target j". + prod = 1.0 + for i in range(weapons): + q = max(1.0 - destruction_probs[i][j], _EPS) + prod *= q ** mu[i] # ← ** mu[i] required by eq (3.14) + prod = max(prod, _MIN_EXP) + + b_t = math.log(prod) + b_list: list[float] = [b_t] + + # Iteratively add breakpoints until we reach 0 + while b_t < -_EPS: + current_b = b_t # remember before entering the closure - return tuple(normalized_assignment) + # Step 1 (eq. 5.3): + # Find x > current_b where the tangent drawn at current_b reaches an error of delta. + # Tangent at current_b: L(x) = exp(current_b) + exp(current_b)*(x - current_b) + # Condition: exp(x) - L(x) = delta + def f1(x_val, _b=current_b): + xf = float(x_val) + return math.exp(xf) - (math.exp(_b) + math.exp(_b) * (xf - _b)) - delta + try: + x1 = float(broyden1(f1, 1.0, f_tol=1e-15)) + except Exception: + x1 = 0.0 + + b_next = x1 if x1 < 0 else 0.0 + b_list.append(b_next) + b_t = b_next + + if b_t < -_EPS: + current_b = b_t + + # Step 2 (eq. 5.4): + # Find the tangent point x* > current_b for the next segment. + # Condition: exp(x) - (exp(current_b) + exp(x)*(x - current_b)) + delta = 0 + def f2(x_val, _b=current_b): + xf = float(x_val) + return math.exp(xf) - (math.exp(_b) + math.exp(xf) * (xf - _b)) + delta + + try: + x2 = float(broyden1(f2, 1.0, f_tol=1e-15)) + except Exception: + x2 = 0.0 + + b_t = x2 if x2 < 0 else 0.0 + + # Add to list only when we reach 0 (last breakpoint) + if abs(b_t) < _EPS: + b_list.append(0.0) + + return b_list + + +# --------------------------------------------------------------------------- +# Solver 1: WTA_A (upper approximation, section 3) — uses PuLP/CBC +# --------------------------------------------------------------------------- def solve_exact( - instance: WTAInstance, - num_piecewise_segments: int = 20, - warm_start: WTASolution | Sequence[Sequence[int]] | None = None, - time_limit_seconds: float | None = None, + instance: WTAInstance, + num_piecewise_segments: int = 20, + warm_start: WTASolution | Sequence[Sequence[int]] | None = None, + time_limit_seconds: float | None = None, ) -> WTASolution: """ - Solve the Static WTA problem using an exact MILP formulation via pulp. - Uses log transformation mapping and piecewise linear approximation for the - exponential objective reduction. + Approximate MILP (WTA_A from Section 3 of Andersen) solved via PuLP/CBC. + + Uses a convex overapproximation of exp(y) by tangents — gives an upper bound + on the objective value (does not solve the problem exactly). """ start = perf_counter() warm_start_assignment = _normalize_warm_start(instance, warm_start) - prob = pulp.LpProblem("Static_WTA_MIP", pulp.LpMinimize) - - # Decision variables - x = [[pulp.LpVariable(f"x_{i}_{j}", cat=pulp.LpBinary) - for j in range(instance.targets)] for i in range(instance.weapons)] + prob = pulp.LpProblem("WTA_A", pulp.LpMinimize) + x = [ + [pulp.LpVariable(f"x_{i}_{j}", cat=pulp.LpBinary) for j in range(instance.targets)] + for i in range(instance.weapons) + ] z = [pulp.LpVariable(f"z_{j}", lowBound=0.0) for j in range(instance.targets)] y = [pulp.LpVariable(f"y_{j}") for j in range(instance.targets)] @@ -59,34 +153,24 @@ def solve_exact( for j in range(instance.targets): x[i][j].setInitialValue(warm_start_assignment[i][j]) - # Objective: Minimize sum(V_j * z_j) where z_j ~ exp(y_j) - prob += pulp.lpSum([instance.target_values[j] * z[j] for j in range(instance.targets)]) + prob += pulp.lpSum(instance.target_values[j] * z[j] for j in range(instance.targets)) - # Each weapon assigned at most once for i in range(instance.weapons): prob += pulp.lpSum(x[i]) <= 1 - EPSILON = 1e-9 # to prevent log(0) if p=1.0 - for j in range(instance.targets): - ln_q = [] - for i in range(instance.weapons): - q_ij = 1.0 - instance.destruction_probabilities[i][j] - q_ij = max(q_ij, EPSILON) - ln_q.append(math.log(q_ij)) - - # y_j = sum_i( x_{ij} * ln(q_ij) ) - prob += y[j] == pulp.lpSum([x[i][j] * ln_q[i] for i in range(instance.weapons)]) - - # Piecewise linear envelope for exp(y_j) (since exp is convex, max of tangents is a lower bound) - min_y_j = sum(val for val in ln_q if val < 0) + ln_q = [ + math.log(max(1.0 - instance.destruction_probabilities[i][j], _EPS)) + for i in range(instance.weapons) + ] + prob += y[j] == pulp.lpSum(x[i][j] * ln_q[i] for i in range(instance.weapons)) + min_y_j = sum(v for v in ln_q if v < 0) if min_y_j < 0: step = abs(min_y_j) / max(1, num_piecewise_segments - 1) for k in range(num_piecewise_segments): pk = min_y_j + k * step exp_pk = math.exp(pk) - # Tangent line: z >= exp(pk) + exp(pk) * (y - pk) prob += z[j] >= exp_pk + exp_pk * (y[j] - pk) else: prob += z[j] >= 1.0 @@ -100,179 +184,286 @@ def solve_exact( prob.solve(solver) solver_status = pulp.LpStatus.get(prob.status, f"status_{prob.status}") - assignment = [[0 for _ in range(instance.targets)] for _ in range(instance.weapons)] + assignment = [[0] * instance.targets for _ in range(instance.weapons)] for i in range(instance.weapons): for j in range(instance.targets): if pulp.value(x[i][j]) is not None and pulp.value(x[i][j]) > 0.5: assignment[i][j] = 1 - frozen_assignment = tuple(tuple(row) for row in assignment) - + frozen = tuple(tuple(row) for row in assignment) runtime = perf_counter() - start + return WTASolution( - assignment=frozen_assignment, - objective_value=objective_value(instance, frozen_assignment), + assignment=frozen, + objective_value=objective_value(instance, frozen), runtime_seconds=runtime, - method="exact_mip_pulp_linearized_warm_start" if warm_start_assignment is not None else "exact_mip_pulp_linearized", + method="exact_mip_pulp_linearized_warm_start" if warm_start_assignment is not None + else "exact_mip_pulp_linearized", status=solver_status, ) +# --------------------------------------------------------------------------- +# Solver 2: Branch-and-Adjust (WTA_LA, Section 6 of Andersen) — uses Gurobi +# --------------------------------------------------------------------------- + def solve_branch_and_adjust( instance: WTAInstance, - num_piecewise_segments: int = 20, + delta: float = 1e-4, warm_start: WTASolution | Sequence[Sequence[int]] | None = None, time_limit_seconds: float = 5400.0, ) -> WTASolution: """ - Solve the Static WTA problem using the true Branch-and-Adjust algorithm via Gurobi. - Uses continuous Piecewise Linear Under-Approximations and Lazy Constraints to - dynamically "Adjust" the node objectives when integer solutions are found. + Exact Branch-and-Adjust algorithm (Section 6 of Andersen et al. 2022). + + Uses a compact convex underapproximation of the WTA objective function (WTA_LA) + as a lower bound in the B&B tree. Upon finding an integer solution, + it calculates its TRUE nonlinear value and updates the upper bound. + + Key components: + - Lambda variables with breakpoints from the delta-based procedure (eq. 5.2-5.4) + - Lambda objective coefficients from eq. (5.5) / get_lbda_obj from original CPLEX + - MIPSOL Callback: calculates true WTA, adds lazy cuts forcing + the real z[j] value for the assignment found + - BranchPriority: proxy for the branching strategy from Algorithm 1 (line 13) + + Parameters + ---------- + instance : WTA instance + delta : approximation precision parameter (smaller = more exact, slower) + warm_start : optional starting assignment + time_limit_seconds : time limit in seconds """ start = perf_counter() warm_start_assignment = _normalize_warm_start(instance, warm_start) - # Używamy context managerów Gurobi (dobre praktyki zarządzania licencją) + # mu[i] = 1 for all weapons (by default; extend WTAInstance if needed) + mu: list[int] = getattr(instance, "mu", [1] * instance.weapons) + with gp.Env(empty=True) as env: - env.setParam('OutputFlag', 0) # Ustaw na 1, jeśli chcesz logi Gurobi w konsoli + env.setParam("OutputFlag", 0) env.start() - with gp.Model("WTA_Branch_and_Adjust_Gurobi", env=env) as model: - # Włączamy użycie Lazy Constraints w Gurobi! (KRYTYCZNE) + with gp.Model("WTA_BranchAdjust", env=env) as model: model.Params.LazyConstraints = 1 - if time_limit_seconds is not None: - model.Params.TimeLimit = time_limit_seconds + model.Params.TimeLimit = time_limit_seconds weapons = instance.weapons targets = instance.targets - # Tworzenie zmiennych (używamy słowników dla łatwego dostępu w Gurobi) - x = {} + # ------------------------------------------------------------------ + # Decision variables x[i,j] + # Binary for mu=1; change to INTEGER with ub=mu[i] when mu > 1 + # ------------------------------------------------------------------ + x: dict[tuple[int, int], gp.Var] = {} for i in range(weapons): for j in range(targets): - x[i, j] = model.addVar(vtype=GRB.BINARY, name=f"x_{i}_{j}") + x[i, j] = model.addVar( + vtype=GRB.BINARY, # GRB.INTEGER + ub=mu[i] when mu[i]>1 + name=f"x_{i}_{j}", + ) - y = {} - z = {} + # Each type of weapon used at most mu[i] times in total (eq. 2.3) + for i in range(weapons): + model.addConstr( + gp.quicksum(x[i, j] for j in range(targets)) <= mu[i], + name=f"weapon_{i}", + ) + + # ------------------------------------------------------------------ + # Delta-based breakpoints (procedure from Section 5, eq. 3.14 + 5.2-5.4) + # ------------------------------------------------------------------ + B: list[list[float]] = [] + for j in range(targets): + b_j = _compute_breakpoints(instance.destruction_probabilities, mu, j, delta) + B.append(b_j) + + # ------------------------------------------------------------------ + # Lambda variables lambda[j,t] — convex combination on breakpoints + # Objective coefficients per get_lbda_obj (eq. 5.5 + Section 5): + # t == 0 : w_j * exp(b[j,0]) + # 0 < t < last : w_j * (exp(b[j,t]) - delta) ← correction -delta + # t == last : w_j * 1.0 ← exp(0) = 1 + # ------------------------------------------------------------------ + lbda: dict[tuple[int, int], gp.Var] = {} + for j in range(targets): + w_j = instance.target_values[j] + n_t = len(B[j]) + for t, b_val in enumerate(B[j]): + if t == 0: + obj_coef = w_j * math.exp(b_val) + elif t < n_t - 1: + obj_coef = w_j * (math.exp(b_val) - delta) + else: + obj_coef = w_j * 1.0 + lbda[j, t] = model.addVar(lb=0.0, obj=obj_coef, name=f"lbda_{j}_{t}") + + model.ModelSense = GRB.MINIMIZE + + # ------------------------------------------------------------------ + # Constraints for lambda variables + # ------------------------------------------------------------------ + for j in range(targets): + n_t = len(B[j]) + + # (5.12): sum_t lambda[j,t] = 1 + model.addConstr( + gp.quicksum(lbda[j, t] for t in range(n_t)) == 1.0, + name=f"sc3_{j}", + ) + + # (5.11): sum_t lambda[j,t]*b[j,t] = sum_i ln(1-p[i,j]) * x[i,j] + lhs = gp.quicksum(B[j][t] * lbda[j, t] for t in range(n_t)) + rhs = gp.quicksum( + math.log(max(1.0 - instance.destruction_probabilities[i][j], _EPS)) * x[i, j] + for i in range(weapons) + ) + model.addConstr(lhs == rhs, name=f"sc2_{j}") + + # ------------------------------------------------------------------ + # Auxiliary variable z[j] = approximated weighted cost of target j + # Needed in the callback to compare with true_cost_j = w_j * true_z_j + # ------------------------------------------------------------------ + z: dict[int, gp.Var] = {} for j in range(targets): - y[j] = model.addVar(lb=-GRB.INFINITY, name=f"y_{j}") z[j] = model.addVar(lb=0.0, name=f"z_{j}") - - # Cel - minimalizacja przybliżonych kosztów Z - model.setObjective(gp.quicksum(instance.target_values[j] * z[j] for j in range(targets)), GRB.MINIMIZE) - - # Każda broń użyta max raz + # z[j] >= sum of coefficients * lambda (lower link to approximation) + under_approx = gp.quicksum( + ( + instance.target_values[j] * math.exp(B[j][t]) if t == 0 + else instance.target_values[j] * (math.exp(B[j][t]) - delta) if t < len(B[j]) - 1 + else instance.target_values[j] * 1.0 + ) * lbda[j, t] + for t in range(len(B[j])) + ) + model.addConstr(z[j] >= under_approx, name=f"z_link_{j}") + + # ------------------------------------------------------------------ + # Branching priorities (proxy for Algorithm 1, line 13) + # Variables with larger |w_j * ln(1-p_ij)| branched first + # ------------------------------------------------------------------ for i in range(weapons): - model.addConstr(gp.quicksum(x[i, j] for j in range(targets)) <= 1, name=f"w_{i}") - - EPSILON = 1e-9 - - # Przybliżenie logarytmiczne i styczne pod krzywą (WTA_LA z artykułu) - for j in range(targets): - ln_q = [] - for i in range(weapons): - q_ij = 1.0 - instance.destruction_probabilities[i][j] - ln_q.append(math.log(max(q_ij, EPSILON))) - - model.addConstr(y[j] == gp.quicksum(x[i, j] * ln_q[i] for i in range(weapons)), name=f"y_def_{j}") - - min_y_j = sum(val for val in ln_q if val < 0) - - if min_y_j < 0: - step = abs(min_y_j) / max(1, num_piecewise_segments - 1) - for k in range(num_piecewise_segments): - pk = min_y_j + k * step - exp_pk = math.exp(pk) - model.addConstr(z[j] >= exp_pk + exp_pk * (y[j] - pk)) - else: - model.addConstr(z[j] >= 1.0) + for j in range(targets): + coef = instance.target_values[j] * math.log( + max(1.0 - instance.destruction_probabilities[i][j], _EPS) + ) + x[i, j].BranchPriority = int(abs(coef) * 1000) - # Inicjacja rozwiązania (Warm Start) + # Warm start if warm_start_assignment is not None: for i in range(weapons): for j in range(targets): x[i, j].Start = warm_start_assignment[i][j] - # Rejestrujemy obiekty w modelu, żeby Callback miał do nich dostęp + # Pass references to the callback via model attributes model._x = x model._z = z + model._B = B model._instance = instance - model._best_true_obj = float('inf') + model._best_true_obj = float("inf") model._best_assignment = None - # --------------------------------------------------------- - # MAGIA BRANCH-AND-ADJUST: GUROBI LAZY CALLBACK - # --------------------------------------------------------- + # ------------------------------------------------------------------ + # CALLBACK: Branch-and-Adjust (equivalent to CPLEX IncumbentCallback) + # + # When Gurobi finds an integer solution x*: + # 1. Calculate the TRUE nonlinear WTA(x*) value + # 2. Update best_true_obj and best_assignment + # 3. For each j where z[j] < w_j*true_z[j]: + # Add lazy cut: z[j] >= true_cost_j * (1 - delta_x) + # When delta_x = 0 (same x*): forces z[j] = true_cost_j + # When delta_x >= 1 (other x) : non-binding constraint (>=0) + # 4. Gurobi re-evaluates the node with the new constraint and accepts + # the incumbent with the true cost — replaces CPLEX reject()+inject() + # ------------------------------------------------------------------ def bna_callback(cb_model, where): - # Gdy solver zatrzyma się na pełnym węźle całkowitoliczbowym - if where == GRB.Callback.MIPSOL: - # Pobieramy to próbne przypisanie i jego przybliżone koszty - x_val = cb_model.cbGetSolution(cb_model._x) - z_val = cb_model.cbGetSolution(cb_model._z) - - targets = cb_model._instance.targets - weapons = cb_model._instance.weapons - - true_z = [1.0] * targets - S1_vars = [] - S0_vars = [] - - # Liczymy prawdziwy nieliniowy wzór ułożenia - for i in range(weapons): - for j in range(targets): - if x_val[i, j] > 0.5: - true_z[j] *= (1.0 - cb_model._instance.destruction_probabilities[i][j]) - S1_vars.append(cb_model._x[i, j]) - else: - S0_vars.append(cb_model._x[i, j]) - - true_obj = sum(cb_model._instance.target_values[j] * true_z[j] for j in range(targets)) - - # Notujemy sobie ten układ w razie czego (gdyby limit czasu zablokował solver) - if true_obj < cb_model._best_true_obj: - cb_model._best_true_obj = true_obj - assignment = [[0 for _ in range(targets)] for _ in range(weapons)] - for i in range(weapons): - for j in range(targets): - if x_val[i, j] > 0.5: - assignment[i][j] = 1 - cb_model._best_assignment = tuple(tuple(row) for row in assignment) - - # KROK ADJUST: Sprawdzamy czy przybliżony model zaniżył koszty. - # Zamiast 'reject()', w Gurobi wysyłamy poprawkę kosztu dla TEGO KONKRETNEGO ułożenia. - delta_x = None # Wskaźnik, który wynosi 0 TYLKO wtedy, gdy ułożenie to dokładnie to obecne + if where != GRB.Callback.MIPSOL: + return + + x_val: dict = cb_model.cbGetSolution(cb_model._x) + z_val: dict = cb_model.cbGetSolution(cb_model._z) + + inst = cb_model._instance + n_w = inst.weapons + n_t = inst.targets + + # True survival value of each target: prod_i (1-p_ij)^x_ij + true_z = [1.0] * n_t + assigned: list[tuple[int, int]] = [] # (i,j) where x[i,j] = 1 + not_assigned: list[tuple[int, int]] = [] + + for i in range(n_w): + for j in range(n_t): + val = x_val[i, j] + if val > 0.5: + # For binary variables val ≈ 1; for integer val = r + true_z[j] *= (1.0 - inst.destruction_probabilities[i][j]) ** round(val) + assigned.append((i, j)) + else: + not_assigned.append((i, j)) + + true_obj = sum(inst.target_values[j] * true_z[j] for j in range(n_t)) + + # Update best solution + if true_obj < cb_model._best_true_obj: + cb_model._best_true_obj = true_obj + cb_model._best_assignment = tuple( + tuple( + 1 if (i, j) in set(assigned) else 0 + for j in range(n_t) + ) + for i in range(n_w) + ) + + # Build Hamming distance expression: + # delta_x = sum_{(i,j) in S1} (1 - x[i,j]) + sum_{(i,j) in S0} x[i,j] + # delta_x = 0 ↔ current x = x* + # delta_x >= 1 ↔ differs in at least one position + delta_x_expr: gp.LinExpr | None = None + + for j in range(n_t): + true_cost_j = inst.target_values[j] * true_z[j] + + # Add cut only when the model underestimated target j cost + if z_val[j] < true_cost_j - 1e-6: + if delta_x_expr is None: + # Create Hamming expression once, reuse for all j + delta_x_expr = gp.quicksum( + 1 - cb_model._x[i, j2] for (i, j2) in assigned + ) + gp.quicksum( + cb_model._x[i, j2] for (i, j2) in not_assigned + ) + + # Lazy cut (eq. key Adjust step): + # z[j] >= true_cost_j * (1 - delta_x) + # When delta_x = 0: z[j] >= true_cost_j → solver will use true cost + # When delta_x >= 1: z[j] >= 0 → non-binding + cb_model.cbLazy( + cb_model._z[j] >= true_cost_j * (1.0 - delta_x_expr) + ) - for j in range(targets): - if z_val[j] < true_z[j] - 1e-6: # Model skłamał (zaniżył) o więcej niż EPSILON - if delta_x is None: - # Konstrukcja odległości Hamminga dla macierzy binarnej w Gurobi - delta_x = len(S1_vars) - gp.quicksum(S1_vars) + gp.quicksum(S0_vars) - - # Dodajemy tzw. cięcie no-good cut. - # Jeśli badane ułożenie jest dokładnie tym, które znaleźliśmy (delta_x == 0), - # to wymuszamy matematycznie: Z musi podskoczyć do Prawdziwego Z. - # Dla innych ułożeń (delta >= 1), równanie się kasuje do 0 i nie przeszkadza drzewu. - cb_model.cbLazy(cb_model._z[j] >= true_z[j] * (1.0 - delta_x)) - - # Start optymalizacji wraz z "pilnującym" go callbackiem model.optimize(bna_callback) + # ------------------------------------------------------------------ + # Collect results + # ------------------------------------------------------------------ runtime = perf_counter() - start - status = model.Status status_map = { GRB.OPTIMAL: "optimal", GRB.TIME_LIMIT: "time_limit", GRB.INFEASIBLE: "infeasible", + GRB.INF_OR_UNBD: "inf_or_unbd", } - status_str = status_map.get(status, f"status_{status}") + status_str = status_map.get(model.Status, f"status_{model.Status}") if model._best_assignment is not None: final_assignment = model._best_assignment final_obj = model._best_true_obj else: + # Fallback: no integer solution was found final_assignment = tuple(tuple(0 for _ in range(targets)) for _ in range(weapons)) - final_obj = float('inf') + final_obj = float("inf") return WTASolution( assignment=final_assignment, @@ -280,4 +471,4 @@ def bna_callback(cb_model, where): runtime_seconds=runtime, method="branch_and_adjust_gurobi", status=status_str, - ) \ No newline at end of file + ) From 6fca754882802a79f69c9d8e730993845cb934c7 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Sun, 24 May 2026 11:27:04 +0200 Subject: [PATCH 12/13] target function fix memory naming conv --- benchmark.py | 2 +- src/wta_optimization/exact.py | 228 +++++++++++++++++++++------------- 2 files changed, 141 insertions(+), 89 deletions(-) diff --git a/benchmark.py b/benchmark.py index 769b216..99ae47c 100644 --- a/benchmark.py +++ b/benchmark.py @@ -21,7 +21,7 @@ ("ls", "Greedy + Local Search", "tab:blue"), ("sa", "Simulated Annealing", "tab:green"), ("exact", "Exact MIP (PuLP)", "black"), - ("bna", "Branch & Adjust (SCIP)", "tab:red"), + ("bna", "Branch & Adjust (Gurobi)", "tab:red"), ] EXACT_TIME_LIMIT_SECONDS = 5400.0 diff --git a/src/wta_optimization/exact.py b/src/wta_optimization/exact.py index e6f03ef..09808bc 100644 --- a/src/wta_optimization/exact.py +++ b/src/wta_optimization/exact.py @@ -216,28 +216,44 @@ def solve_branch_and_adjust( """ Exact Branch-and-Adjust algorithm (Section 6 of Andersen et al. 2022). - Uses a compact convex underapproximation of the WTA objective function (WTA_LA) - as a lower bound in the B&B tree. Upon finding an integer solution, - it calculates its TRUE nonlinear value and updates the upper bound. - - Key components: - - Lambda variables with breakpoints from the delta-based procedure (eq. 5.2-5.4) - - Lambda objective coefficients from eq. (5.5) / get_lbda_obj from original CPLEX - - MIPSOL Callback: calculates true WTA, adds lazy cuts forcing - the real z[j] value for the assignment found - - BranchPriority: proxy for the branching strategy from Algorithm 1 (line 13) + Uses a compact convex underapproximation of the WTA objective (WTA_LA) + as a lower bound in the B&B tree. When an integer solution x* is found, + its TRUE nonlinear WTA value is computed and used to update the upper bound. + + Model structure (WTA_BranchAdjust_Int from original CPLEX code): + - x[i,j] : integer assignment variables (binary for mu=1) + - lbda[j,t] : convex combination weights on breakpoints (no obj coefficient) + - z[j] : weighted approximated cost for target j — IN THE OBJECTIVE + z[j] >= under_approx(j) via z_link constraint + - Objective : minimize sum_j z[j] + + Objective coefficients per get_lbda_obj (eq. 5.5): + t == 0 : w_j * exp(b[j,0]) + 0 < t < last : w_j * (exp(b[j,t]) - delta) ← -delta correction required + t == last : w_j * 1.0 ← exp(0) = 1 + + Adjust callback (equivalent to CPLEX IncumbentCallback + HeuristicCallback): + When Gurobi finds integer x* with approx cost < true WTA(x*): + - Compute true WTA(x*) + - Update best_true_obj / best_assignment + - Add lazy cut: z[j] >= true_cost_j * (1 - delta_x) + delta_x = 0 when x = x* → z[j] >= true_cost_j + delta_x >= 1 otherwise → constraint non-binding + - Gurobi re-evaluates x* with z[j] forced to true_cost_j + → objective = sum true_cost_j = true WTA(x*) + → Gurobi records TRUE cost as incumbent value ✓ Parameters ---------- - instance : WTA instance - delta : approximation precision parameter (smaller = more exact, slower) - warm_start : optional starting assignment - time_limit_seconds : time limit in seconds + instance : WTA instance + delta : approximation precision (smaller = more exact, slower) + warm_start : optional starting assignment + time_limit_seconds : time limit in seconds """ start = perf_counter() warm_start_assignment = _normalize_warm_start(instance, warm_start) - # mu[i] = 1 for all weapons (by default; extend WTAInstance if needed) + # mu[i] = 1 for all weapons by default; set instance.mu for mu > 1 mu: list[int] = getattr(instance, "mu", [1] * instance.weapons) with gp.Env(empty=True) as env: @@ -253,17 +269,17 @@ def solve_branch_and_adjust( # ------------------------------------------------------------------ # Decision variables x[i,j] - # Binary for mu=1; change to INTEGER with ub=mu[i] when mu > 1 + # Binary when mu[i]=1 (default); use GRB.INTEGER + ub=mu[i] for mu>1 # ------------------------------------------------------------------ x: dict[tuple[int, int], gp.Var] = {} for i in range(weapons): for j in range(targets): x[i, j] = model.addVar( - vtype=GRB.BINARY, # GRB.INTEGER + ub=mu[i] when mu[i]>1 + vtype=GRB.BINARY, name=f"x_{i}_{j}", ) - # Each type of weapon used at most mu[i] times in total (eq. 2.3) + # Constraint (2.3): each weapon type used at most mu[i] times total for i in range(weapons): model.addConstr( gp.quicksum(x[i, j] for j in range(targets)) <= mu[i], @@ -271,32 +287,54 @@ def solve_branch_and_adjust( ) # ------------------------------------------------------------------ - # Delta-based breakpoints (procedure from Section 5, eq. 3.14 + 5.2-5.4) + # Delta-based breakpoints per target (Section 5, eq. 3.14 + 5.2-5.4) # ------------------------------------------------------------------ B: list[list[float]] = [] for j in range(targets): - b_j = _compute_breakpoints(instance.destruction_probabilities, mu, j, delta) + b_j = _compute_breakpoints( + instance.destruction_probabilities, mu, j, delta + ) B.append(b_j) # ------------------------------------------------------------------ - # Lambda variables lambda[j,t] — convex combination on breakpoints - # Objective coefficients per get_lbda_obj (eq. 5.5 + Section 5): - # t == 0 : w_j * exp(b[j,0]) - # 0 < t < last : w_j * (exp(b[j,t]) - delta) ← correction -delta - # t == last : w_j * 1.0 ← exp(0) = 1 + # Lambda variables — convex combination weights on breakpoints. + # NO objective coefficient here: the objective goes through z[j]. # ------------------------------------------------------------------ lbda: dict[tuple[int, int], gp.Var] = {} for j in range(targets): + for t in range(len(B[j])): + lbda[j, t] = model.addVar(lb=0.0, name=f"lbda_{j}_{t}") + + # ------------------------------------------------------------------ + # z[j] — weighted approximated cost of target j, IN THE OBJECTIVE. + # This is the variable that gets forced up by lazy cuts to the true + # cost, making Gurobi record the TRUE incumbent value. + # ------------------------------------------------------------------ + z: dict[int, gp.Var] = {} + for j in range(targets): + # obj=1.0: z[j] is directly in the objective (minimize sum z[j]) + z[j] = model.addVar(lb=0.0, obj=1.0, name=f"z_{j}") + w_j = instance.target_values[j] n_t = len(B[j]) - for t, b_val in enumerate(B[j]): - if t == 0: - obj_coef = w_j * math.exp(b_val) - elif t < n_t - 1: - obj_coef = w_j * (math.exp(b_val) - delta) - else: - obj_coef = w_j * 1.0 - lbda[j, t] = model.addVar(lb=0.0, obj=obj_coef, name=f"lbda_{j}_{t}") + + # Under-approximation coefficients per get_lbda_obj (eq. 5.5): + # t == 0 : w_j * exp(b[j,0]) + # 0 < t < last : w_j * (exp(b[j,t]) - delta) + # t == last : w_j * 1.0 + under_approx = gp.quicksum( + ( + w_j * math.exp(B[j][t]) if t == 0 + else w_j * (math.exp(B[j][t]) - delta) if t < n_t - 1 + else w_j * 1.0 + ) * lbda[j, t] + for t in range(n_t) + ) + + # z[j] >= under-approximation of w_j * survival[j] + # When lazy cut later forces z[j] >= true_cost_j for x*, + # the objective correctly reflects the true WTA value. + model.addConstr(z[j] >= under_approx, name=f"z_link_{j}") model.ModelSense = GRB.MINIMIZE @@ -306,7 +344,7 @@ def solve_branch_and_adjust( for j in range(targets): n_t = len(B[j]) - # (5.12): sum_t lambda[j,t] = 1 + # (5.12): sum_t lambda[j,t] = 1 (convex combination) model.addConstr( gp.quicksum(lbda[j, t] for t in range(n_t)) == 1.0, name=f"sc3_{j}", @@ -315,32 +353,17 @@ def solve_branch_and_adjust( # (5.11): sum_t lambda[j,t]*b[j,t] = sum_i ln(1-p[i,j]) * x[i,j] lhs = gp.quicksum(B[j][t] * lbda[j, t] for t in range(n_t)) rhs = gp.quicksum( - math.log(max(1.0 - instance.destruction_probabilities[i][j], _EPS)) * x[i, j] + math.log(max(1.0 - instance.destruction_probabilities[i][j], _EPS)) + * x[i, j] for i in range(weapons) ) model.addConstr(lhs == rhs, name=f"sc2_{j}") - # ------------------------------------------------------------------ - # Auxiliary variable z[j] = approximated weighted cost of target j - # Needed in the callback to compare with true_cost_j = w_j * true_z_j - # ------------------------------------------------------------------ - z: dict[int, gp.Var] = {} - for j in range(targets): - z[j] = model.addVar(lb=0.0, name=f"z_{j}") - # z[j] >= sum of coefficients * lambda (lower link to approximation) - under_approx = gp.quicksum( - ( - instance.target_values[j] * math.exp(B[j][t]) if t == 0 - else instance.target_values[j] * (math.exp(B[j][t]) - delta) if t < len(B[j]) - 1 - else instance.target_values[j] * 1.0 - ) * lbda[j, t] - for t in range(len(B[j])) - ) - model.addConstr(z[j] >= under_approx, name=f"z_link_{j}") - # ------------------------------------------------------------------ # Branching priorities (proxy for Algorithm 1, line 13) - # Variables with larger |w_j * ln(1-p_ij)| branched first + # Variables with larger |w_j * ln(1-p_ij)| branched first — + # matches CPLEX BranchCallback's "unfixed variable with smallest + # objective coefficient" strategy. # ------------------------------------------------------------------ for i in range(weapons): for j in range(targets): @@ -355,26 +378,39 @@ def solve_branch_and_adjust( for j in range(targets): x[i, j].Start = warm_start_assignment[i][j] - # Pass references to the callback via model attributes + # Precompute sum of all x variables for efficient Hamming distance in callback. + # This avoids building a 500k-term expression inside the callback. + all_x_sum = gp.quicksum( + x[i, j] for i in range(weapons) for j in range(targets) + ) + + # Pass references to callback via model attributes model._x = x model._z = z model._B = B model._instance = instance + model._all_x_sum = all_x_sum model._best_true_obj = float("inf") model._best_assignment = None # ------------------------------------------------------------------ - # CALLBACK: Branch-and-Adjust (equivalent to CPLEX IncumbentCallback) + # CALLBACK: Branch-and-Adjust + # (equivalent to CPLEX IncumbentCallback + HeuristicCallback) # - # When Gurobi finds an integer solution x*: - # 1. Calculate the TRUE nonlinear WTA(x*) value + # When Gurobi finds integer solution x*: + # 1. Compute TRUE nonlinear WTA(x*) # 2. Update best_true_obj and best_assignment - # 3. For each j where z[j] < w_j*true_z[j]: + # 3. For each j where z[j] < w_j * true_z[j] (model underestimated): # Add lazy cut: z[j] >= true_cost_j * (1 - delta_x) - # When delta_x = 0 (same x*): forces z[j] = true_cost_j - # When delta_x >= 1 (other x) : non-binding constraint (>=0) - # 4. Gurobi re-evaluates the node with the new constraint and accepts - # the incumbent with the true cost — replaces CPLEX reject()+inject() + # - delta_x = 0 (same x*) → z[j] >= true_cost_j + # - delta_x >= 1 (other x) → z[j] >= 0 (non-binding) + # 4. Gurobi re-evaluates x* with z[j] forced to true_cost_j + # → objective = sum_j true_cost_j = TRUE WTA(x*) + # → Gurobi records incumbent with TRUE cost ✓ + # + # This replaces CPLEX's three-callback mechanism: + # IncumbentCallback.reject() + HeuristicCallback.set_solution() + # + BranchCallback for integer-node branching # ------------------------------------------------------------------ def bna_callback(cb_model, where): if where != GRB.Callback.MIPSOL: @@ -387,57 +423,71 @@ def bna_callback(cb_model, where): n_w = inst.weapons n_t = inst.targets - # True survival value of each target: prod_i (1-p_ij)^x_ij + # Compute TRUE survival probability: prod_i (1 - p_ij)^x_ij true_z = [1.0] * n_t - assigned: list[tuple[int, int]] = [] # (i,j) where x[i,j] = 1 - not_assigned: list[tuple[int, int]] = [] + assigned: list[tuple[int, int]] = [] # (i,j) where x[i,j] = 1 for i in range(n_w): for j in range(n_t): val = x_val[i, j] if val > 0.5: - # For binary variables val ≈ 1; for integer val = r - true_z[j] *= (1.0 - inst.destruction_probabilities[i][j]) ** round(val) + true_z[j] *= ( + (1.0 - inst.destruction_probabilities[i][j]) + ** round(val) + ) assigned.append((i, j)) - else: - not_assigned.append((i, j)) - true_obj = sum(inst.target_values[j] * true_z[j] for j in range(n_t)) + true_obj = sum( + inst.target_values[j] * true_z[j] for j in range(n_t) + ) - # Update best solution + # Track best true solution found so far if true_obj < cb_model._best_true_obj: cb_model._best_true_obj = true_obj + assigned_set = set(assigned) cb_model._best_assignment = tuple( tuple( - 1 if (i, j) in set(assigned) else 0 + 1 if (i, j) in assigned_set else 0 for j in range(n_t) ) for i in range(n_w) ) - # Build Hamming distance expression: - # delta_x = sum_{(i,j) in S1} (1 - x[i,j]) + sum_{(i,j) in S0} x[i,j] - # delta_x = 0 ↔ current x = x* - # delta_x >= 1 ↔ differs in at least one position + # Build Hamming distance expression (computed once, reused for all j): + # delta_x = |S1| - 2*sum_{S1} x[i,j] + sum_{all} x[i,j] + # delta_x = 0 ↔ current x equals x* + # delta_x >= 1 ↔ differs in at least one position + # + # O(|assigned|) terms — avoids building a ~500k-term expression + # by exploiting: sum_{S0} x[i,j] = sum_{all} x[i,j] - sum_{S1} x[i,j] delta_x_expr: gp.LinExpr | None = None for j in range(n_t): true_cost_j = inst.target_values[j] * true_z[j] - # Add cut only when the model underestimated target j cost + # Add cut only when model underestimated cost for target j if z_val[j] < true_cost_j - 1e-6: if delta_x_expr is None: - # Create Hamming expression once, reuse for all j - delta_x_expr = gp.quicksum( - 1 - cb_model._x[i, j2] for (i, j2) in assigned - ) + gp.quicksum( - cb_model._x[i, j2] for (i, j2) in not_assigned + # Efficient Hamming distance using precomputed all_x_sum + delta_x_expr = ( + len(assigned) + - 2 * gp.quicksum( + cb_model._x[i, j2] for (i, j2) in assigned + ) + + cb_model._all_x_sum ) - # Lazy cut (eq. key Adjust step): + # KEY ADJUST STEP: # z[j] >= true_cost_j * (1 - delta_x) - # When delta_x = 0: z[j] >= true_cost_j → solver will use true cost - # When delta_x >= 1: z[j] >= 0 → non-binding + # + # When delta_x = 0 (same assignment): + # z[j] >= true_cost_j + # → objective = sum z[j] = sum true_cost_j = TRUE WTA(x*) + # → Gurobi accepts incumbent with TRUE cost + # + # When delta_x >= 1 (different assignment): + # z[j] >= true_cost_j * (1 - delta_x) <= 0 + # → non-binding (z[j] >= 0 already) cb_model.cbLazy( cb_model._z[j] >= true_cost_j * (1.0 - delta_x_expr) ) @@ -461,8 +511,10 @@ def bna_callback(cb_model, where): final_assignment = model._best_assignment final_obj = model._best_true_obj else: - # Fallback: no integer solution was found - final_assignment = tuple(tuple(0 for _ in range(targets)) for _ in range(weapons)) + # Fallback: no integer solution found within time limit + final_assignment = tuple( + tuple(0 for _ in range(targets)) for _ in range(weapons) + ) final_obj = float("inf") return WTASolution( From 36166d548ed8b44cd9ebb1c41b38dd13ddf30662 Mon Sep 17 00:00:00 2001 From: Norbert Frydrysiak Date: Sun, 24 May 2026 13:20:13 +0200 Subject: [PATCH 13/13] not binary fix branch and adjust --- src/wta_optimization/exact.py | 515 ++++++++++++++++++++-------------- 1 file changed, 304 insertions(+), 211 deletions(-) diff --git a/src/wta_optimization/exact.py b/src/wta_optimization/exact.py index 09808bc..621f063 100644 --- a/src/wta_optimization/exact.py +++ b/src/wta_optimization/exact.py @@ -207,296 +207,376 @@ def solve_exact( # Solver 2: Branch-and-Adjust (WTA_LA, Section 6 of Andersen) — uses Gurobi # --------------------------------------------------------------------------- +def _resolve_mu( + instance: WTAInstance, + mu: Sequence[int] | None = None, +) -> list[int]: + """ + Resolve weapon-type availabilities for the full Andersen WTA model. + + The original WTAInstance used in this project stores only a probability row + per weapon/weapon type, but it does not store the Andersen parameter mu_i. + Therefore Branch-and-Adjust accepts mu explicitly. For backwards + compatibility, if mu is not provided and the instance has no mu-like + attribute, the solver uses mu_i = 1 for every row. + """ + raw_mu = mu + if raw_mu is None: + raw_mu = getattr(instance, "mu", None) + if raw_mu is None: + raw_mu = getattr(instance, "weapon_availabilities", None) + if raw_mu is None: + raw_mu = getattr(instance, "availabilities", None) + if raw_mu is None: + raw_mu = [1] * instance.weapons + + resolved = [int(v) for v in raw_mu] + if len(resolved) != instance.weapons: + raise ValueError("mu must have one value for each weapon type / probability row") + if any(v < 0 for v in resolved): + raise ValueError("all mu values must be non-negative integers") + return resolved + + +def _normalize_integer_warm_start( + instance: WTAInstance, + warm_start: WTASolution | Sequence[Sequence[int]] | None, + mu: Sequence[int], +) -> tuple[tuple[int, ...], ...] | None: + """ + Normalize a warm start for the full integer WTA model. + + Unlike _normalize_warm_start(), this function deliberately preserves counts + greater than 1. It is used only by Branch-and-Adjust. + """ + if warm_start is None: + return None + + assignment = warm_start.assignment if isinstance(warm_start, WTASolution) else warm_start + if len(assignment) != instance.weapons: + raise ValueError("warm start assignment row count must match the number of weapon types") + + normalized: list[tuple[int, ...]] = [] + for i, row in enumerate(assignment): + if len(row) != instance.targets: + raise ValueError("each warm start row must match the number of targets") + + normalized_row: list[int] = [] + row_sum = 0 + for value in row: + rounded = int(round(float(value))) + if abs(float(value) - rounded) > 1e-7: + raise ValueError("warm start values for Branch-and-Adjust must be integers") + if rounded < 0 or rounded > mu[i]: + raise ValueError("warm start value outside [0, mu_i]") + normalized_row.append(rounded) + row_sum += rounded + + if row_sum > mu[i]: + raise ValueError("warm start uses more weapons of a type than allowed by mu_i") + normalized.append(tuple(normalized_row)) + + return tuple(normalized) + + +def _integer_assignment_objective( + instance: WTAInstance, + assignment: Sequence[Sequence[int]], +) -> float: + """True nonlinear WTA objective for integer assignments x_ij.""" + total = 0.0 + for j, target_value in enumerate(instance.target_values): + survived_value = float(target_value) + for i in range(instance.weapons): + count = int(assignment[i][j]) + if count > 0: + survived_value *= (1.0 - instance.destruction_probabilities[i][j]) ** count + total += survived_value + return total + + def solve_branch_and_adjust( instance: WTAInstance, delta: float = 1e-4, warm_start: WTASolution | Sequence[Sequence[int]] | None = None, time_limit_seconds: float = 5400.0, + mu: Sequence[int] | None = None, ) -> WTASolution: """ - Exact Branch-and-Adjust algorithm (Section 6 of Andersen et al. 2022). - - Uses a compact convex underapproximation of the WTA objective (WTA_LA) - as a lower bound in the B&B tree. When an integer solution x* is found, - its TRUE nonlinear WTA value is computed and used to update the upper bound. - - Model structure (WTA_BranchAdjust_Int from original CPLEX code): - - x[i,j] : integer assignment variables (binary for mu=1) - - lbda[j,t] : convex combination weights on breakpoints (no obj coefficient) - - z[j] : weighted approximated cost for target j — IN THE OBJECTIVE - z[j] >= under_approx(j) via z_link constraint - - Objective : minimize sum_j z[j] - - Objective coefficients per get_lbda_obj (eq. 5.5): - t == 0 : w_j * exp(b[j,0]) - 0 < t < last : w_j * (exp(b[j,t]) - delta) ← -delta correction required - t == last : w_j * 1.0 ← exp(0) = 1 - - Adjust callback (equivalent to CPLEX IncumbentCallback + HeuristicCallback): - When Gurobi finds integer x* with approx cost < true WTA(x*): - - Compute true WTA(x*) - - Update best_true_obj / best_assignment - - Add lazy cut: z[j] >= true_cost_j * (1 - delta_x) - delta_x = 0 when x = x* → z[j] >= true_cost_j - delta_x >= 1 otherwise → constraint non-binding - - Gurobi re-evaluates x* with z[j] forced to true_cost_j - → objective = sum true_cost_j = true WTA(x*) - → Gurobi records TRUE cost as incumbent value ✓ - - Parameters - ---------- - instance : WTA instance - delta : approximation precision (smaller = more exact, slower) - warm_start : optional starting assignment - time_limit_seconds : time limit in seconds + Branch-and-Adjust for the full integer WTA model from Andersen et al. (2022). + + This implementation supports x[i,j] > 1, i.e. assigning multiple weapons of + the same type to the same target. The Andersen parameter mu_i is resolved as: + + 1. the explicit `mu=` argument, if provided; + 2. instance.mu / instance.weapon_availabilities / instance.availabilities, + if such an attribute exists; + 3. [1, ..., 1] for backwards compatibility with the original project. + + Mathematical model: + x[i,j] integer in {0, ..., mu[i]} + sum_j x[i,j] <= mu[i] + sum_t lambda[j,t] = 1 + sum_t b[j,t] lambda[j,t] = sum_i log(1-p[i,j]) x[i,j] + z[j] >= compact under-approximation of w[j] * exp(log-survival[j]) + min sum_j z[j] + + Adjustment step: + Whenever Gurobi finds an integer assignment x*, the callback computes + the true nonlinear WTA value. If z[j] underestimates the true survived + value of target j, a lazy cut is added that is active only for exactly + this integer assignment x*. + + Important implementation detail: + For x[i,j] with mu[i] > 1, one-hot value indicators are added: + is_value[i,j,k] = 1 iff x[i,j] = k. + These indicators make the lazy adjustment cut valid for integer-count + assignments, not only for binary assignments. """ + if delta <= 0: + raise ValueError("delta must be positive for Branch-and-Adjust") + start = perf_counter() - warm_start_assignment = _normalize_warm_start(instance, warm_start) + mu_values = _resolve_mu(instance, mu) + warm_start_assignment = _normalize_integer_warm_start(instance, warm_start, mu_values) - # mu[i] = 1 for all weapons by default; set instance.mu for mu > 1 - mu: list[int] = getattr(instance, "mu", [1] * instance.weapons) + weapons = instance.weapons + targets = instance.targets with gp.Env(empty=True) as env: env.setParam("OutputFlag", 0) env.start() - with gp.Model("WTA_BranchAdjust", env=env) as model: + with gp.Model("WTA_BranchAdjust_Integer", env=env) as model: model.Params.LazyConstraints = 1 - model.Params.TimeLimit = time_limit_seconds - - weapons = instance.weapons - targets = instance.targets + if time_limit_seconds is not None: + model.Params.TimeLimit = time_limit_seconds - # ------------------------------------------------------------------ + # -------------------------------------------------------------- # Decision variables x[i,j] - # Binary when mu[i]=1 (default); use GRB.INTEGER + ub=mu[i] for mu>1 - # ------------------------------------------------------------------ + # -------------------------------------------------------------- x: dict[tuple[int, int], gp.Var] = {} + x_keys: list[tuple[int, int]] = [] + x_vars: list[gp.Var] = [] + for i in range(weapons): for j in range(targets): - x[i, j] = model.addVar( - vtype=GRB.BINARY, - name=f"x_{i}_{j}", - ) + if mu_values[i] == 1: + var = model.addVar(vtype=GRB.BINARY, name=f"x_{i}_{j}") + else: + var = model.addVar( + vtype=GRB.INTEGER, + lb=0.0, + ub=float(mu_values[i]), + name=f"x_{i}_{j}", + ) + x[i, j] = var + x_keys.append((i, j)) + x_vars.append(var) - # Constraint (2.3): each weapon type used at most mu[i] times total + # Weapon availability constraints: sum_j x[i,j] <= mu_i for i in range(weapons): model.addConstr( - gp.quicksum(x[i, j] for j in range(targets)) <= mu[i], - name=f"weapon_{i}", + gp.quicksum(x[i, j] for j in range(targets)) <= mu_values[i], + name=f"weapon_availability_{i}", ) - # ------------------------------------------------------------------ - # Delta-based breakpoints per target (Section 5, eq. 3.14 + 5.2-5.4) - # ------------------------------------------------------------------ + # -------------------------------------------------------------- + # One-hot value indicators for integer variables with mu_i > 1. + # Needed to build an exact-match lazy cut for integer assignments. + # -------------------------------------------------------------- + value_is: dict[tuple[int, int, int], gp.Var] = {} + for i in range(weapons): + if mu_values[i] <= 1: + continue + for j in range(targets): + indicators = [] + for k in range(mu_values[i] + 1): + ind = model.addVar( + vtype=GRB.BINARY, + name=f"x_is_{i}_{j}_{k}", + ) + value_is[i, j, k] = ind + indicators.append(ind) + + model.addConstr( + gp.quicksum(indicators) == 1, + name=f"x_value_onehot_{i}_{j}", + ) + model.addConstr( + x[i, j] == gp.quicksum(k * value_is[i, j, k] for k in range(mu_values[i] + 1)), + name=f"x_value_link_{i}_{j}", + ) + + # -------------------------------------------------------------- + # Delta-based breakpoints per target. + # b[j,0] = log(prod_i (1-p_ij)^mu_i), final breakpoint = 0. + # -------------------------------------------------------------- B: list[list[float]] = [] for j in range(targets): - b_j = _compute_breakpoints( - instance.destruction_probabilities, mu, j, delta - ) - B.append(b_j) + B.append(_compute_breakpoints(instance.destruction_probabilities, mu_values, j, delta)) - # ------------------------------------------------------------------ - # Lambda variables — convex combination weights on breakpoints. - # NO objective coefficient here: the objective goes through z[j]. - # ------------------------------------------------------------------ + # -------------------------------------------------------------- + # Lambda and z variables for compact convex under-approximation. + # -------------------------------------------------------------- lbda: dict[tuple[int, int], gp.Var] = {} for j in range(targets): for t in range(len(B[j])): lbda[j, t] = model.addVar(lb=0.0, name=f"lbda_{j}_{t}") - # ------------------------------------------------------------------ - # z[j] — weighted approximated cost of target j, IN THE OBJECTIVE. - # This is the variable that gets forced up by lazy cuts to the true - # cost, making Gurobi record the TRUE incumbent value. - # ------------------------------------------------------------------ z: dict[int, gp.Var] = {} + z_vars: list[gp.Var] = [] for j in range(targets): - # obj=1.0: z[j] is directly in the objective (minimize sum z[j]) z[j] = model.addVar(lb=0.0, obj=1.0, name=f"z_{j}") + z_vars.append(z[j]) w_j = instance.target_values[j] - n_t = len(B[j]) + last_t = len(B[j]) - 1 - # Under-approximation coefficients per get_lbda_obj (eq. 5.5): - # t == 0 : w_j * exp(b[j,0]) - # 0 < t < last : w_j * (exp(b[j,t]) - delta) - # t == last : w_j * 1.0 + # Coefficients from Andersen's compact under-approximation: + # first: w_j exp(b_0), middle: w_j(exp(b_t)-delta), last: w_j. under_approx = gp.quicksum( ( w_j * math.exp(B[j][t]) if t == 0 - else w_j * (math.exp(B[j][t]) - delta) if t < n_t - 1 - else w_j * 1.0 + else w_j * (math.exp(B[j][t]) - delta) if t < last_t + else w_j ) * lbda[j, t] - for t in range(n_t) + for t in range(len(B[j])) ) + model.addConstr(z[j] >= under_approx, name=f"z_underapprox_link_{j}") - # z[j] >= under-approximation of w_j * survival[j] - # When lazy cut later forces z[j] >= true_cost_j for x*, - # the objective correctly reflects the true WTA value. - model.addConstr(z[j] >= under_approx, name=f"z_link_{j}") - - model.ModelSense = GRB.MINIMIZE - - # ------------------------------------------------------------------ - # Constraints for lambda variables - # ------------------------------------------------------------------ + # Lambda convexity and log-survival linking constraints. for j in range(targets): - n_t = len(B[j]) - - # (5.12): sum_t lambda[j,t] = 1 (convex combination) model.addConstr( - gp.quicksum(lbda[j, t] for t in range(n_t)) == 1.0, - name=f"sc3_{j}", + gp.quicksum(lbda[j, t] for t in range(len(B[j]))) == 1.0, + name=f"lambda_sum_{j}", ) - # (5.11): sum_t lambda[j,t]*b[j,t] = sum_i ln(1-p[i,j]) * x[i,j] - lhs = gp.quicksum(B[j][t] * lbda[j, t] for t in range(n_t)) + lhs = gp.quicksum(B[j][t] * lbda[j, t] for t in range(len(B[j]))) rhs = gp.quicksum( - math.log(max(1.0 - instance.destruction_probabilities[i][j], _EPS)) - * x[i, j] + math.log(max(1.0 - instance.destruction_probabilities[i][j], _EPS)) * x[i, j] for i in range(weapons) ) - model.addConstr(lhs == rhs, name=f"sc2_{j}") - - # ------------------------------------------------------------------ - # Branching priorities (proxy for Algorithm 1, line 13) - # Variables with larger |w_j * ln(1-p_ij)| branched first — - # matches CPLEX BranchCallback's "unfixed variable with smallest - # objective coefficient" strategy. - # ------------------------------------------------------------------ + model.addConstr(lhs == rhs, name=f"log_survival_link_{j}") + + model.ModelSense = GRB.MINIMIZE + + # Branching priorities: prefer variables with greater objective impact. for i in range(weapons): for j in range(targets): - coef = instance.target_values[j] * math.log( - max(1.0 - instance.destruction_probabilities[i][j], _EPS) + coef = abs( + instance.target_values[j] + * math.log(max(1.0 - instance.destruction_probabilities[i][j], _EPS)) ) - x[i, j].BranchPriority = int(abs(coef) * 1000) + x[i, j].BranchPriority = int(min(2_000_000_000, coef * 1000)) - # Warm start + # Warm start, preserving integer counts. if warm_start_assignment is not None: for i in range(weapons): for j in range(targets): - x[i, j].Start = warm_start_assignment[i][j] - - # Precompute sum of all x variables for efficient Hamming distance in callback. - # This avoids building a 500k-term expression inside the callback. - all_x_sum = gp.quicksum( - x[i, j] for i in range(weapons) for j in range(targets) - ) - - # Pass references to callback via model attributes + start_value = warm_start_assignment[i][j] + x[i, j].Start = start_value + if mu_values[i] > 1: + for k in range(mu_values[i] + 1): + value_is[i, j, k].Start = 1.0 if k == start_value else 0.0 + + # -------------------------------------------------------------- + # Callback state. + # -------------------------------------------------------------- model._x = x + model._x_keys = x_keys + model._x_vars = x_vars model._z = z - model._B = B + model._z_vars = z_vars + model._value_is = value_is + model._mu_values = mu_values model._instance = instance - model._all_x_sum = all_x_sum + model._total_x_positions = weapons * targets model._best_true_obj = float("inf") model._best_assignment = None + model._lazy_cuts_added = 0 + + def _match_indicator_expr(cb_model, i: int, j: int, value: int): + """ + Linear expression equal to 1 iff x[i,j] equals `value`, for + integer-feasible solutions. Uses x itself when mu_i = 1 and + one-hot indicators when mu_i > 1. + """ + mu_i = cb_model._mu_values[i] + if mu_i == 0: + return 1.0 + if mu_i == 1: + return cb_model._x[i, j] if value == 1 else 1.0 - cb_model._x[i, j] + return cb_model._value_is[i, j, value] - # ------------------------------------------------------------------ - # CALLBACK: Branch-and-Adjust - # (equivalent to CPLEX IncumbentCallback + HeuristicCallback) - # - # When Gurobi finds integer solution x*: - # 1. Compute TRUE nonlinear WTA(x*) - # 2. Update best_true_obj and best_assignment - # 3. For each j where z[j] < w_j * true_z[j] (model underestimated): - # Add lazy cut: z[j] >= true_cost_j * (1 - delta_x) - # - delta_x = 0 (same x*) → z[j] >= true_cost_j - # - delta_x >= 1 (other x) → z[j] >= 0 (non-binding) - # 4. Gurobi re-evaluates x* with z[j] forced to true_cost_j - # → objective = sum_j true_cost_j = TRUE WTA(x*) - # → Gurobi records incumbent with TRUE cost ✓ - # - # This replaces CPLEX's three-callback mechanism: - # IncumbentCallback.reject() + HeuristicCallback.set_solution() - # + BranchCallback for integer-node branching - # ------------------------------------------------------------------ def bna_callback(cb_model, where): if where != GRB.Callback.MIPSOL: return - x_val: dict = cb_model.cbGetSolution(cb_model._x) - z_val: dict = cb_model.cbGetSolution(cb_model._z) - inst = cb_model._instance + mu_cb = cb_model._mu_values n_w = inst.weapons n_t = inst.targets - # Compute TRUE survival probability: prod_i (1 - p_ij)^x_ij - true_z = [1.0] * n_t - assigned: list[tuple[int, int]] = [] # (i,j) where x[i,j] = 1 - - for i in range(n_w): - for j in range(n_t): - val = x_val[i, j] - if val > 0.5: - true_z[j] *= ( - (1.0 - inst.destruction_probabilities[i][j]) - ** round(val) - ) - assigned.append((i, j)) - - true_obj = sum( - inst.target_values[j] * true_z[j] for j in range(n_t) - ) - - # Track best true solution found so far - if true_obj < cb_model._best_true_obj: + x_values = cb_model.cbGetSolution(cb_model._x_vars) + z_values = cb_model.cbGetSolution(cb_model._z_vars) + + # Build the integer assignment x* and compute the true nonlinear + # WTA value: sum_j w_j prod_i (1-p_ij)^x_ij. + current_assignment = [[0 for _ in range(n_t)] for _ in range(n_w)] + true_survival = [1.0 for _ in range(n_t)] + + for idx, (i, j) in enumerate(cb_model._x_keys): + value = int(round(x_values[idx])) + if value < 0: + value = 0 + elif value > mu_cb[i]: + value = mu_cb[i] + + current_assignment[i][j] = value + if value > 0: + true_survival[j] *= ( + 1.0 - inst.destruction_probabilities[i][j] + ) ** value + + true_target_cost = [ + inst.target_values[j] * true_survival[j] + for j in range(n_t) + ] + true_obj = sum(true_target_cost) + + if true_obj < cb_model._best_true_obj - 1e-9: cb_model._best_true_obj = true_obj - assigned_set = set(assigned) cb_model._best_assignment = tuple( - tuple( - 1 if (i, j) in assigned_set else 0 - for j in range(n_t) - ) - for i in range(n_w) + tuple(row) for row in current_assignment ) - # Build Hamming distance expression (computed once, reused for all j): - # delta_x = |S1| - 2*sum_{S1} x[i,j] + sum_{all} x[i,j] - # delta_x = 0 ↔ current x equals x* - # delta_x >= 1 ↔ differs in at least one position - # - # O(|assigned|) terms — avoids building a ~500k-term expression - # by exploiting: sum_{S0} x[i,j] = sum_{all} x[i,j] - sum_{S1} x[i,j] - delta_x_expr: gp.LinExpr | None = None - - for j in range(n_t): - true_cost_j = inst.target_values[j] * true_z[j] - - # Add cut only when model underestimated cost for target j - if z_val[j] < true_cost_j - 1e-6: - if delta_x_expr is None: - # Efficient Hamming distance using precomputed all_x_sum - delta_x_expr = ( - len(assigned) - - 2 * gp.quicksum( - cb_model._x[i, j2] for (i, j2) in assigned - ) - + cb_model._all_x_sum - ) - - # KEY ADJUST STEP: - # z[j] >= true_cost_j * (1 - delta_x) - # - # When delta_x = 0 (same assignment): - # z[j] >= true_cost_j - # → objective = sum z[j] = sum true_cost_j = TRUE WTA(x*) - # → Gurobi accepts incumbent with TRUE cost - # - # When delta_x >= 1 (different assignment): - # z[j] >= true_cost_j * (1 - delta_x) <= 0 - # → non-binding (z[j] >= 0 already) - cb_model.cbLazy( - cb_model._z[j] >= true_cost_j * (1.0 - delta_x_expr) - ) + # Exact-match distance for integer assignments: + # delta_x = 0 iff every x[i,j] equals its current integer value; + # delta_x >= 1 otherwise. + # Then z[j] >= true_cost_j * (1 - delta_x) is active only at x*. + needs_adjustment = [ + j for j in range(n_t) + if z_values[j] < true_target_cost[j] - 1e-6 + ] + if not needs_adjustment: + return + + match_count = gp.quicksum( + _match_indicator_expr(cb_model, i, j, current_assignment[i][j]) + for i in range(n_w) + for j in range(n_t) + ) + delta_x = cb_model._total_x_positions - match_count + + for j in needs_adjustment: + cb_model.cbLazy( + cb_model._z[j] >= true_target_cost[j] * (1.0 - delta_x) + ) + cb_model._lazy_cuts_added += 1 model.optimize(bna_callback) - # ------------------------------------------------------------------ - # Collect results - # ------------------------------------------------------------------ runtime = perf_counter() - start status_map = { @@ -504,14 +584,27 @@ def bna_callback(cb_model, where): GRB.TIME_LIMIT: "time_limit", GRB.INFEASIBLE: "infeasible", GRB.INF_OR_UNBD: "inf_or_unbd", + GRB.UNBOUNDED: "unbounded", + GRB.INTERRUPTED: "interrupted", + GRB.NODE_LIMIT: "node_limit", + GRB.SOLUTION_LIMIT: "solution_limit", + GRB.ITERATION_LIMIT: "iteration_limit", } status_str = status_map.get(model.Status, f"status_{model.Status}") if model._best_assignment is not None: final_assignment = model._best_assignment final_obj = model._best_true_obj + elif model.SolCount > 0: + # Fallback should rarely be needed, because every integer solution + # should pass through MIPSOL. It is kept for robustness. + final_assignment_list = [[0 for _ in range(targets)] for _ in range(weapons)] + for i in range(weapons): + for j in range(targets): + final_assignment_list[i][j] = int(round(x[i, j].X)) + final_assignment = tuple(tuple(row) for row in final_assignment_list) + final_obj = _integer_assignment_objective(instance, final_assignment) else: - # Fallback: no integer solution found within time limit final_assignment = tuple( tuple(0 for _ in range(targets)) for _ in range(weapons) ) @@ -521,6 +614,6 @@ def bna_callback(cb_model, where): assignment=final_assignment, objective_value=final_obj, runtime_seconds=runtime, - method="branch_and_adjust_gurobi", + method="branch_and_adjust_gurobi_integer", status=status_str, )