public void ConstructorTest2() { Accord.Math.Tools.SetupGenerator(0); var function = new NonlinearObjectiveFunction(2, function: x => x[0] * x[1], gradient: x => new[] { x[1], x[0] }); NonlinearConstraint[] constraints = { new NonlinearConstraint(function, function: x => 1.0 - x[0] * x[0] - x[1] * x[1], gradient: x => new [] { -2 * x[0], -2 * x[1] }), new NonlinearConstraint(function, function: x => x[0], gradient: x => new [] { 1.0, 0.0 }), }; var target = new ConjugateGradient(2); AugmentedLagrangian solver = new AugmentedLagrangian(target, function, constraints); Assert.IsTrue(solver.Minimize()); double minimum = solver.Value; double[] solution = solver.Solution; double sqrthalf = Math.Sqrt(0.5); Assert.AreEqual(-0.5, minimum, 1e-5); Assert.AreEqual(sqrthalf, solution[0], 1e-5); Assert.AreEqual(-sqrthalf, solution[1], 1e-5); double expectedMinimum = function.Function(solver.Solution); Assert.AreEqual(expectedMinimum, minimum); }
public void ConstructorTest3() { // minimize f(x) = x*y*z, // s.t. // // 1 - x² - 2y² - 3z² > 0 // x > 0, // y > 0 // // Easy three dimensional minimization in ellipsoid. var function = new NonlinearObjectiveFunction(3, function: x => x[0] * x[1] * x[2], gradient: x => new[] { x[1] * x[2], x[0] * x[2], x[0] * x[1] }); NonlinearConstraint[] constraints = { new NonlinearConstraint(3, function: x => 1.0 - x[0] * x[0] - 2.0 * x[1] * x[1] - 3.0 * x[2] * x[2], gradient: x => new[] { -2.0 * x[0], -4.0 * x[1], -6.0 * x[2] }), new NonlinearConstraint(3, function: x => x[0], gradient: x => new[] { 1.0, 0, 0 }), new NonlinearConstraint(3, function: x => x[1], gradient: x => new[] { 0, 1.0, 0 }), new NonlinearConstraint(3, function: x => - x[2], gradient: x => new[] { 0, 0, -1.0 }), }; for (int i = 0; i < constraints.Length; i++) { Assert.AreEqual(ConstraintType.GreaterThanOrEqualTo, constraints[i].ShouldBe); Assert.AreEqual(0, constraints[i].Value); } var inner = new BroydenFletcherGoldfarbShanno(3); inner.LineSearch = LineSearch.BacktrackingArmijo; inner.Corrections = 10; var solver = new AugmentedLagrangian(inner, function, constraints); Assert.AreEqual(inner, solver.Optimizer); Assert.IsTrue(solver.Minimize()); double minimum = solver.Value; double[] solution = solver.Solution; double[] expected = { 1.0 / Math.Sqrt(3.0), 1.0 / Math.Sqrt(6.0), -1.0 / 3.0 }; for (int i = 0; i < expected.Length; i++) { Assert.AreEqual(expected[i], solver.Solution[i], 1e-3); } Assert.AreEqual(-0.078567420132031968, minimum, 1e-4); double expectedMinimum = function.Function(solver.Solution); Assert.AreEqual(expectedMinimum, minimum); }
private static void test2(IGradientOptimizationMethod inner) { // maximize 2x + 3y, s.t. 2x² + 2y² <= 50 // // http://www.wolframalpha.com/input/?i=max+2x+%2B+3y%2C+s.t.+2x%C2%B2+%2B+2y%C2%B2+%3C%3D+50 // Max x' * c // x // s.t. x' * A * x <= k // x' * i = 1 // lower_bound < x < upper_bound double[] c = { 2, 3 }; double[,] A = { { 2, 0 }, { 0, 2 } }; double k = 50; // Create the objective function var objective = new NonlinearObjectiveFunction(2, function: (x) => x.InnerProduct(c), gradient: (x) => c ); // Test objective for (int i = 0; i < 10; i++) { for (int j = 0; j < 10; j++) { double expected = i * 2 + j * 3; double actual = objective.Function(new double[] { i, j }); Assert.AreEqual(expected, actual); } } // Create the optimization constraints var constraints = new List <NonlinearConstraint>(); constraints.Add(new QuadraticConstraint(objective, quadraticTerms: A, shouldBe: ConstraintType.LesserThanOrEqualTo, value: k )); // Test first constraint for (int i = 0; i < 10; i++) { for (int j = 0; j < 10; j++) { var input = new double[] { i, j }; double expected = i * (2 * i + 0 * j) + j * (0 * i + 2 * j); double actual = constraints[0].Function(input); Assert.AreEqual(expected, actual); } } // Create the solver algorithm AugmentedLagrangian solver = new AugmentedLagrangian(inner, objective, constraints); Assert.AreEqual(inner, solver.Optimizer); Assert.IsTrue(solver.Maximize()); double maxValue = solver.Value; Assert.AreEqual(18.02, maxValue, 1e-2); Assert.AreEqual(2.77, solver.Solution[0], 1e-2); Assert.AreEqual(4.16, solver.Solution[1], 1e-2); }
private static void test1(IGradientOptimizationMethod inner, double tol) { // maximize 2x + 3y, s.t. 2x² + 2y² <= 50 and x+y = 1 // Max x' * c // x // s.t. x' * A * x <= k // x' * i = 1 // lower_bound < x < upper_bound double[] c = { 2, 3 }; double[,] A = { { 2, 0 }, { 0, 2 } }; double k = 50; // Create the objective function var objective = new NonlinearObjectiveFunction(2, function: (x) => x.InnerProduct(c), gradient: (x) => c ); // Test objective for (int i = 0; i < 10; i++) { for (int j = 0; j < 10; j++) { double expected = i * 2 + j * 3; double actual = objective.Function(new double[] { i, j }); Assert.AreEqual(expected, actual); } } // Create the optimization constraints var constraints = new List <NonlinearConstraint>(); constraints.Add(new QuadraticConstraint(objective, quadraticTerms: A, shouldBe: ConstraintType.LesserThanOrEqualTo, value: k )); constraints.Add(new NonlinearConstraint(objective, function: (x) => x.Sum(), gradient: (x) => new[] { 1.0, 1.0 }, shouldBe: ConstraintType.EqualTo, value: 1, withinTolerance: 1e-10 )); // Test first constraint for (int i = 0; i < 10; i++) { for (int j = 0; j < 10; j++) { double expected = i * (2 * i + 0 * j) + j * (0 * i + 2 * j); double actual = constraints[0].Function(new double[] { i, j }); Assert.AreEqual(expected, actual); } } // Test second constraint for (int i = 0; i < 10; i++) { for (int j = 0; j < 10; j++) { double expected = i + j; double actual = constraints[1].Function(new double[] { i, j }); Assert.AreEqual(expected, actual); } } AugmentedLagrangian solver = new AugmentedLagrangian(inner, objective, constraints); Assert.AreEqual(inner, solver.Optimizer); Assert.IsTrue(solver.Maximize()); double maxValue = solver.Value; Assert.AreEqual(6, maxValue, tol); Assert.AreEqual(-3, solver.Solution[0], tol); Assert.AreEqual(4, solver.Solution[1], tol); }
public void AugmentedLagrangianSolverConstructorTest5() { // Suppose we would like to minimize the following function: // // f(x,y) = min 100(y-x²)²+(1-x)² // // Subject to the constraints // // x >= 0 (x must be positive) // y >= 0 (y must be positive) // double x = 0, y = 0; // First, we create our objective function var f = new NonlinearObjectiveFunction( // This is the objective function: f(x,y) = min 100(y-x²)²+(1-x)² function: () => 100 * Math.Pow(y - x * x, 2) + Math.Pow(1 - x, 2), // The gradient vector: gradient: () => new[] { 2 * (200 * Math.Pow(x, 3) - 200 * x * y + x - 1), // df/dx = 2(200x³-200xy+x-1) 200 * (y - x * x) // df/dy = 200(y-x²) } ); // Now we can start stating the constraints var constraints = new List <NonlinearConstraint>(); // Add the non-negativity constraint for x constraints.Add(new NonlinearConstraint(f, // 1st constraint: x should be greater than or equal to 0 function: () => x, shouldBe: ConstraintType.GreaterThanOrEqualTo, value: 0, gradient: () => new[] { 1.0, 0.0 } )); // Add the non-negativity constraint for y constraints.Add(new NonlinearConstraint(f, // 2nd constraint: y should be greater than or equal to 0 function: () => y, shouldBe: ConstraintType.GreaterThanOrEqualTo, value: 0, gradient: () => new[] { 0.0, 1.0 } )); // Finally, we create the non-linear programming solver var solver = new AugmentedLagrangian(f, constraints); // And attempt to solve the problem Assert.IsTrue(solver.Minimize()); double minValue = solver.Value; Assert.AreEqual(0, minValue, 1e-10); Assert.AreEqual(1, solver.Solution[0], 1e-6); Assert.AreEqual(1, solver.Solution[1], 1e-6); Assert.IsFalse(Double.IsNaN(minValue)); Assert.IsFalse(Double.IsNaN(solver.Solution[0])); Assert.IsFalse(Double.IsNaN(solver.Solution[1])); }
public void AugmentedLagrangianSolverConstructorTest4() { // min x*y+ y*z // // s.t. x^2 - y^2 + z^2 - 2 >= 0 // x^2 + y^2 + z^2 - 10 <= 0 // x + y = 1 // double x = 0, y = 0, z = 0; var f = new NonlinearObjectiveFunction( function: () => x * y + y * z, gradient: () => new[] { y, // df/dx x + z, // df/dy y, // df/dz } ); var constraints = new List <NonlinearConstraint>(); constraints.Add(new NonlinearConstraint(f, function: () => x * x - y * y + z * z, gradient: () => new[] { 2 * x, -2 * y, 2 * z }, shouldBe: ConstraintType.GreaterThanOrEqualTo, value: 2 )); constraints.Add(new NonlinearConstraint(f, function: () => x * x + y * y + z * z, gradient: () => new[] { 2 * x, 2 * y, 2 * z }, shouldBe: ConstraintType.LesserThanOrEqualTo, value: 10 )); constraints.Add(new NonlinearConstraint(f, function: () => x + y, gradient: () => new[] { 1.0, 1.0, 0.0 }, shouldBe: ConstraintType.EqualTo, value: 1 ) { Tolerance = 1e-5 }); var solver = new AugmentedLagrangian(f, constraints); solver.Solution[0] = 1; solver.Solution[1] = 1; solver.Solution[2] = 1; Assert.IsTrue(solver.Minimize()); double minValue = solver.Value; Assert.AreEqual(1, solver.Solution[0] + solver.Solution[1], 1e-4); Assert.IsFalse(Double.IsNaN(minValue)); Assert.IsFalse(Double.IsNaN(solver.Solution[0])); Assert.IsFalse(Double.IsNaN(solver.Solution[1])); Assert.IsFalse(Double.IsNaN(solver.Solution[2])); }
public void constructorTest5() { // AugmentedLagrangian with NonlinearConstraints // have a Gradient NullReferenceException issue // https://github.com/accord-net/framework/issues/177 // Easy three dimensional minimization in ellipsoid. var function = new NonlinearObjectiveFunction(3, function: x => x[0] * x[1] * x[2], gradient: x => new[] { x[1] * x[2], x[0] * x[2], x[0] * x[1] }); NonlinearConstraint[] constraints = { new NonlinearConstraint(function, constraint: x => (1.0 - x[0] * x[0] - 2.0 * x[1] * x[1] - 3.0 * x[2] * x[2]) >= 0, gradient: x => new[] { -2.0 * x[0], -4.0 * x[1], -6.0 * x[2] }), new NonlinearConstraint(function, constraint: x => x[0] >= 0, gradient: x => new[] { 1.0, 0, 0 }), new NonlinearConstraint(function, constraint: x => x[1] >= 0, gradient: x => new[] { 0, 1.0, 0 }), new NonlinearConstraint(function, constraint: x => - x[2] >= 0, gradient: x => new[] { 0, 0, -1.0 }), }; for (int i = 0; i < constraints.Length; i++) { Assert.AreEqual(ConstraintType.GreaterThanOrEqualTo, constraints[i].ShouldBe); Assert.AreEqual(0, constraints[i].Value); } var inner = new BroydenFletcherGoldfarbShanno(3); inner.LineSearch = LineSearch.BacktrackingArmijo; inner.Corrections = 10; var solver = new AugmentedLagrangian(inner, function, constraints); Assert.AreEqual(inner, solver.Optimizer); Assert.IsTrue(solver.Minimize()); double minimum = solver.Value; double[] solution = solver.Solution; double[] expected = { 1.0 / Math.Sqrt(3.0), 1.0 / Math.Sqrt(6.0), -1.0 / 3.0 }; for (int i = 0; i < expected.Length; i++) { Assert.AreEqual(expected[i], solver.Solution[i], 1e-3); } Assert.AreEqual(-0.078567420132031968, minimum, 1e-4); double expectedMinimum = function.Function(solver.Solution); Assert.AreEqual(expectedMinimum, minimum); }
public void AugmentedLagrangianSolverConstructorTest5() { #region doc_lambda // Suppose we would like to minimize the following function: // // f(x,y) = min 100(y-x²)²+(1-x)² // // Subject to the constraints // // x >= 0 (x must be positive) // y >= 0 (y must be positive) // // First, let's declare some symbolic variables double x = 0, y = 0; // (values do not matter) // Now, we create an objective function var f = new NonlinearObjectiveFunction( // This is the objective function: f(x,y) = min 100(y-x²)²+(1-x)² function: () => 100 * Math.Pow(y - x * x, 2) + Math.Pow(1 - x, 2), // And this is the vector gradient for the same function: gradient: () => new[] { 2 * (200 * Math.Pow(x, 3) - 200 * x * y + x - 1), // df/dx = 2(200x³-200xy+x-1) 200 * (y - x * x) // df/dy = 200(y-x²) } ); // Now we can start stating the constraints var constraints = new List <NonlinearConstraint>() { // Add the non-negativity constraint for x new NonlinearConstraint(f, // 1st constraint: x should be greater than or equal to 0 function: () => x, shouldBe: ConstraintType.GreaterThanOrEqualTo, value: 0, gradient: () => new[] { 1.0, 0.0 } ), // Add the non-negativity constraint for y new NonlinearConstraint(f, // 2nd constraint: y should be greater than or equal to 0 function: () => y, shouldBe: ConstraintType.GreaterThanOrEqualTo, value: 0, gradient: () => new[] { 0.0, 1.0 } ) }; // Finally, we create the non-linear programming solver var solver = new AugmentedLagrangian(f, constraints); // And attempt to find a minimum bool success = solver.Minimize(); // The solution found was { 1, 1 } double[] solution = solver.Solution; // with the minimum value zero. double minValue = solver.Value; #endregion Assert.IsTrue(success); Assert.AreEqual(0, minValue, 1e-10); Assert.AreEqual(1, solver.Solution[0], 1e-6); Assert.AreEqual(1, solver.Solution[1], 1e-6); Assert.IsFalse(Double.IsNaN(minValue)); Assert.IsFalse(Double.IsNaN(solver.Solution[0])); Assert.IsFalse(Double.IsNaN(solver.Solution[1])); }
public static void AugmentedLagrangianSolverConstructorTest4() { double x = 0, y = 0; var f = new NonlinearObjectiveFunction( function: () => 0.3 * x + 0.6 * y, gradient: () => new[] { 0.3, // df/dx 0.6, // df/dy } ); var constraints = new List <NonlinearConstraint>(); constraints.Add(new NonlinearConstraint(f, function: () => 7 * x + 3 * y, gradient: () => new[] { 7.0, 3.0 }, shouldBe: ConstraintType.GreaterThanOrEqualTo, value: 2100 )); constraints.Add(new NonlinearConstraint(f, function: () => y, gradient: () => new[] { 0, 1.0 }, shouldBe: ConstraintType.GreaterThanOrEqualTo, value: 1200 )); constraints.Add(new NonlinearConstraint(f, function: () => x, gradient: () => new[] { 1.0, 0 }, shouldBe: ConstraintType.GreaterThanOrEqualTo, value: 0 )); var solver = new AugmentedLagrangian(f, constraints); solver.Solution[0] = 1; solver.Solution[1] = 1; bool success = solver.Minimize(); double[] solution = solver.Solution; double minValue = solver.Value; Console.WriteLine("Solver Value = " + solver.Value); Console.WriteLine("Solver Minize = " + solver.Minimize()); Console.WriteLine("Solver Solution x = " + solver.Solution[0]); Console.WriteLine("Solver Solution y = " + solver.Solution[1]); Console.ReadKey(); }