feat(matrix): add QR decomposition algorithm using Gram-Schmidt process

Decomposes a matrix A into an orthogonal matrix Q and an upper triangular
matrix R such that A = Q * R.

### What This Adds

**QRDecomposition.java** - Main algorithm implementation:
- `decompose()` - Performs QR factorization using the Gram-Schmidt process
- Returns a QR object containing both Q (orthogonal) and R (upper triangular) matrices
- Validates input matrix using MatrixUtil.validateInputMatrix()
- Throws ArithmeticException for rank-deficient matrices

**QRDecompositionTest.java** - Unit tests:
- Tests for 2x2 and 3x3 matrix decomposition
- Verifies Q * R reconstruction equals original matrix
- Validates Q columns are orthonormal
- Confirms R is upper triangular
- Tests identity matrix decomposition
- Tests rank-deficient matrix rejection
- Tests null and empty matrix rejection

### Algorithm

The Gram-Schmidt process orthogonalizes columns iteratively:
- For each column j, subtract projections onto previous orthogonal vectors
- Normalize to get j-th column of Q
- Store coefficients in R[i][j]

Time: O(m*n^2) | Space: O(m*n + n^2)

### Reference

https://en.wikipedia.org/wiki/QR_decomposition

Author: MD-Mushfiqur123

src/main/java/com/thealgorithms/matrix/QRDecomposition.java

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+package com.thealgorithms.matrix;
+
+/**
+ * @brief Implementation of QR Decomposition using the Gram-Schmidt process
+ * @details Decomposes a matrix A into an orthogonal matrix Q and an upper
+ * triangular matrix R such that A = Q * R. The Gram-Schmidt process
+ * orthogonalizes the columns of A to produce Q, and R is computed as Q^T * A.
+ * This decomposition is useful for solving linear least squares problems,
+ * eigenvalue computations, and numerical stability in linear algebra.
+ * @see <a href="https://en.wikipedia.org/wiki/QR_decomposition">QR Decomposition</a>
+ */
+public final class QRDecomposition {
+
+    private QRDecomposition() {
+    }
+
+    /**
+     * A helper class to store both Q and R matrices
+     */
+    public static class QR {
+        private final double[][] q;
+        private final double[][] r;
+
+        QR(double[][] q, double[][] r) {
+            this.q = q;
+            this.r = r;
+        }
+
+        public double[][] getQ() {
+            return q;
+        }
+
+        public double[][] getR() {
+            return r;
+        }
+    }
+
+    /**
+     * @brief Performs QR decomposition on a matrix using the Gram-Schmidt process
+     * @param matrix the input matrix (m x n)
+     * @return QR object containing orthogonal matrix Q (m x n) and upper triangular matrix R (n x n)
+     * @throws IllegalArgumentException if the matrix is null, empty, or has invalid rows
+     */
+    public static QR decompose(double[][] matrix) {
+        MatrixUtil.validateInputMatrix(matrix);
+
+        int m = matrix.length;
+        int n = matrix[0].length;
+
+        double[][] q = new double[m][n];
+        double[][] r = new double[n][n];
+
+        for (int j = 0; j < n; j++) {
+            double[] v = getColumn(matrix, j);
+
+            for (int i = 0; i < j; i++) {
+                double[] qi = getColumn(q, i);
+                r[i][j] = dotProduct(qi, v);
+                v = subtractVectors(v, scalarMultiply(qi, r[i][j]));
+            }
+
+            r[j][j] = norm(v);
+            if (r[j][j] == 0) {
+                throw new ArithmeticException("Matrix is rank deficient. Cannot perform QR decomposition.");
+            }
+            double[] qj = scalarMultiply(v, 1.0 / r[j][j]);
+            setColumn(q, j, qj);
+        }
+
+        return new QR(q, r);
+    }
+
+    private static double[] getColumn(double[][] matrix, int col) {
+        int m = matrix.length;
+        double[] column = new double[m];
+        for (int i = 0; i < m; i++) {
+            column[i] = matrix[i][col];
+        }
+        return column;
+    }
+
+    private static void setColumn(double[][] matrix, int col, double[] column) {
+        for (int i = 0; i < matrix.length; i++) {
+            matrix[i][col] = column[i];
+        }
+    }
+
+    private static double dotProduct(double[] a, double[] b) {
+        double sum = 0;
+        for (int i = 0; i < a.length; i++) {
+            sum += a[i] * b[i];
+        }
+        return sum;
+    }
+
+    private static double[] subtractVectors(double[] a, double[] b) {
+        double[] result = new double[a.length];
+        for (int i = 0; i < a.length; i++) {
+            result[i] = a[i] - b[i];
+        }
+        return result;
+    }
+
+    private static double[] scalarMultiply(double[] v, double scalar) {
+        double[] result = new double[v.length];
+        for (int i = 0; i < v.length; i++) {
+            result[i] = v[i] * scalar;
+        }
+        return result;
+    }
+
+    private static double norm(double[] v) {
+        return Math.sqrt(dotProduct(v, v));
+    }
+}

src/test/java/com/thealgorithms/matrix/QRDecompositionTest.java

@@ -0,0 +1,115 @@
+package com.thealgorithms.matrix;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+import org.junit.jupiter.api.Test;
+
+public class QRDecompositionTest {
+
+    private static final double DELTA = 1e-9;
+
+    @Test
+    public void testQRDecomposition2x2() {
+        double[][] matrix = {{12, -51}, {6, 167}};
+        QRDecomposition.QR qr = QRDecomposition.decompose(matrix);
+        double[][] q = qr.getQ();
+        double[][] r = qr.getR();
+
+        double[][] reconstructed = multiplyMatrices(q, r);
+        for (int i = 0; i < matrix.length; i++) {
+            assertArrayEquals(matrix[i], reconstructed[i], DELTA);
+        }
+    }
+
+    @Test
+    public void testQRDecomposition3x3() {
+        double[][] matrix = {{1, 1, 0}, {1, 0, 1}, {0, 1, 1}};
+        QRDecomposition.QR qr = QRDecomposition.decompose(matrix);
+        double[][] q = qr.getQ();
+        double[][] r = qr.getR();
+
+        double[][] reconstructed = multiplyMatrices(q, r);
+        for (int i = 0; i < matrix.length; i++) {
+            assertArrayEquals(matrix[i], reconstructed[i], DELTA);
+        }
+    }
+
+    @Test
+    public void testQROrthogonalColumns() {
+        double[][] matrix = {{1, 1, 0}, {1, 0, 1}, {0, 1, 1}};
+        QRDecomposition.QR qr = QRDecomposition.decompose(matrix);
+        double[][] q = qr.getQ();
+
+        for (int i = 0; i < q[0].length; i++) {
+            for (int j = i; j < q[0].length; j++) {
+                double dot = 0;
+                for (int k = 0; k < q.length; k++) {
+                    dot += q[k][i] * q[k][j];
+                }
+                if (i == j) {
+                    assertArrayEquals(new double[] {1.0}, new double[] {dot}, DELTA);
+                } else {
+                    assertArrayEquals(new double[] {0.0}, new double[] {dot}, DELTA);
+                }
+            }
+        }
+    }
+
+    @Test
+    public void testRIsUpperTriangular() {
+        double[][] matrix = {{12, -51}, {6, 167}};
+        QRDecomposition.QR qr = QRDecomposition.decompose(matrix);
+        double[][] r = qr.getR();
+
+        for (int i = 1; i < r.length; i++) {
+            for (int j = 0; j < i; j++) {
+                assertArrayEquals(new double[] {0.0}, new double[] {r[i][j]}, DELTA);
+            }
+        }
+    }
+
+    @Test
+    public void testQRDecompositionIdentityMatrix() {
+        double[][] matrix = {{1, 0, 0}, {0, 1, 0}, {0, 0, 1}};
+        QRDecomposition.QR qr = QRDecomposition.decompose(matrix);
+        double[][] q = qr.getQ();
+        double[][] r = qr.getR();
+
+        for (int i = 0; i < matrix.length; i++) {
+            assertArrayEquals(matrix[i], q[i], DELTA);
+            assertArrayEquals(matrix[i], r[i], DELTA);
+        }
+    }
+
+    @Test
+    public void testQRDecompositionRankDeficientThrows() {
+        double[][] matrix = {{1, 2}, {2, 4}};
+        assertThrows(ArithmeticException.class, () -> QRDecomposition.decompose(matrix));
+    }
+
+    @Test
+    public void testQRDecompositionNullMatrixThrows() {
+        assertThrows(IllegalArgumentException.class, () -> QRDecomposition.decompose(null));
+    }
+
+    @Test
+    public void testQRDecompositionEmptyMatrixThrows() {
+        assertThrows(IllegalArgumentException.class, () -> QRDecomposition.decompose(new double[0][0]));
+    }
+
+    private static double[][] multiplyMatrices(double[][] a, double[][] b) {
+        int m = a.length;
+        int n = b[0].length;
+        int k = a[0].length;
+        double[][] result = new double[m][n];
+        for (int i = 0; i < m; i++) {
+            for (int j = 0; j < n; j++) {
+                for (int p = 0; p < k; p++) {
+                    result[i][j] += a[i][p] * b[p][j];
+                }
+            }
+        }
+        return result;
+    }
+}