public void RunTest() { double[,] D = Matrix.Identity(3); double[] d = { 0, 5, 0 }; double[,] A = { { -4, 2, 0 }, { -3, 1, -2 }, { 0, 0, 1 }, }; double[] b = { -8, 2, 0 }; GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(3, A.Transpose(), b); double[] expectedSolution = { 0.4761905, 1.0476190, 2.0952381 }; double expected = -2.380952; double actual = target.Minimize(D, d.Multiply(-1)); Assert.AreEqual(expected, actual, 1e-6); Assert.IsFalse(double.IsNaN(actual)); Assert.AreEqual(3, target.Iterations); Assert.AreEqual(0.4761905, target.Solution[0], 1e-6); Assert.AreEqual(1.0476190, target.Solution[1], 1e-6); Assert.AreEqual(2.0952381, target.Solution[2], 0.02); Assert.AreEqual(0.0000000, target.Lagrangian[0], 1e-6); Assert.AreEqual(0.2380952, target.Lagrangian[1], 1e-6); Assert.AreEqual(2.0952381, target.Lagrangian[2], 1e-6); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); }
private static void DoSv(Tuple<double[], double>[] classified) { var stripped = classified.Select(t => new Tuple<double[], double>(t.Item1.Skip(1).ToArray(), t.Item2)) .ToArray(); var q = GetQ(stripped); //all are 0 or more var lcc = new LinearConstraintCollection( Enumerable.Range(0, stripped.Length) .Select(i => new LinearConstraint(1) { VariablesAtIndices = new[] { i }, ShouldBe = ConstraintType.GreaterThanOrEqualTo, Value = 0.0, CombinedAs= new []{1.0} //??? })); //and they zero out with the classificaiton lcc.Add( //new LinearConstraint(stripped.Select(t => t.Item2).ToArray()) { Value = 0, ShouldBe = ConstraintType.EqualTo } new LinearConstraint(stripped.Length) { Value = 0, ShouldBe = ConstraintType.EqualTo, VariablesAtIndices = Enumerable.Range(0, stripped.Length).ToArray(), CombinedAs = stripped.Select(t => t.Item2).ToArray() } ); var solver = new GoldfarbIdnaniQuadraticSolver(stripped.Length, lcc); solver.Minimize(q, neg1Array(stripped.Length)); //b = 1/y - w_*x_ var tmp = solver.Solution.Zip(stripped, (alpha, s) => s.Item1.Select(x => x * alpha * s.Item2)) .Aggregate(VAdd).ToList(); var support = solver.Solution.Zip(stripped, (alpha, s) => Math.Abs(alpha) > 0.0001 ? s : null).Where(x => x != null).ToArray(); var offset = GetOffset(support, tmp); //I'm pretty sure this calculation for b is wrong: you get a different one for each of elements... tmp.Insert(0, offset); var wSupp = Norm( tmp.ToArray()); }
public void GoldfarbIdnaniConstructorTest8() { // Solve the following optimization problem: // // min f(x) = x² + 2xy + y² - y // // s.t. x >= 1 // y >= 1 // double x = 0, y = 0; // http://www.wolframalpha.com/input/?i=min+x%C2%B2+%2B+2xy+%2B+y%C2%B2+-+y%2C+x+%3E%3D+1%2C+y+%3E%3D+1 var f = new QuadraticObjectiveFunction(() => (x * x) + 2 * (x * y) + (y * y) - y); List<LinearConstraint> constraints = new List<LinearConstraint>(); constraints.Add(new LinearConstraint(f, () => x >= 1)); constraints.Add(new LinearConstraint(f, () => y >= 1)); GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(2, constraints); double[,] A = { { 1, 0 }, { 0, 1 }, }; double[] b = { 1, 1, }; Assert.IsTrue(A.IsEqual(target.ConstraintMatrix)); Assert.IsTrue(b.IsEqual(target.ConstraintValues)); double[,] Q = { { 2, 2 }, { 2, 2 }, }; double[] d = { 0, -1 }; var actualQ = f.GetQuadraticTermsMatrix(); var actuald = f.GetLinearTermsVector(); Assert.IsTrue(Q.IsEqual(actualQ)); Assert.IsTrue(d.IsEqual(actuald)); bool thrown = false; try { target.Minimize(f); } catch (NonPositiveDefiniteMatrixException) { thrown = true; } Assert.IsTrue(thrown); }
public void GoldfarbIdnaniConstructorTest7() { // Solve the following optimization problem: // // min f(x) = 3x² + 2xy + 3y² - y // // s.t. x >= 1 // y >= 1 // double x = 0, y = 0; // http://www.wolframalpha.com/input/?i=min+x%C2%B2+%2B+2xy+%2B+y%C2%B2+-+y%2C+x+%3E%3D+1%2C+y+%3E%3D+1 var f = new QuadraticObjectiveFunction(() => 3 * (x * x) + 2 * (x * y) + 3 * (y * y) - y); List<LinearConstraint> constraints = new List<LinearConstraint>(); constraints.Add(new LinearConstraint(f, () => x >= 1)); constraints.Add(new LinearConstraint(f, () => y >= 1)); GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(2, constraints); double[,] A = { { 1, 0 }, { 0, 1 }, }; double[] b = { 1, 1, }; Assert.IsTrue(A.IsEqual(target.ConstraintMatrix)); Assert.IsTrue(b.IsEqual(target.ConstraintValues)); double[,] Q = { { 6, 2 }, { 2, 6 }, }; double[] d = { 0, -1 }; var actualQ = f.GetQuadraticTermsMatrix(); var actuald = f.GetLinearTermsVector(); Assert.IsTrue(Q.IsEqual(actualQ)); Assert.IsTrue(d.IsEqual(actuald)); double minValue = target.Minimize(f); double[] solution = target.Solution; Assert.AreEqual(7, minValue); Assert.AreEqual(1, solution[0]); Assert.AreEqual(1, solution[1]); Assert.AreEqual(8, target.Lagrangian[0], 1e-5); Assert.AreEqual(7, target.Lagrangian[1], 1e-5); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); }
public void GoldfarbIdnaniConstructorTest6() { // min 1x² - 2xy + 3y² +z² - 4x - 5y -z, 6x-7y <= 8, 9x + 1y <= 11, 9x-y <= 11, -z-y = 12 // http://www.wolframalpha.com/input/?i=min+1x%C2%B2+-+2xy+%2B+3y%C2%B2+%2Bz%C2%B2+-+4x+-+5y+-z%2C+6x-7y+%3C%3D+8%2C+9x+%2B+1y+%3C%3D+11%2C+9x-y+%3C%3D+11%2C+-z-y+%3D+12 var f = new QuadraticObjectiveFunction("1x² - 2xy + 3y² + z² - 4x - 5y -z"); List<LinearConstraint> constraints = new List<LinearConstraint>(); constraints.Add(new LinearConstraint(f, "6x-7y <= 8")); constraints.Add(new LinearConstraint(f, "9x + 1y <= 11")); constraints.Add(new LinearConstraint(f, "9x-y <= 11")); constraints.Add(new LinearConstraint(f, "-z-y = 12")); GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(f.NumberOfVariables, constraints); double value = target.Minimize(f); Assert.AreEqual(14376 / 109.0, value, 1e-10); Assert.AreEqual(-186 / 109.0, target.Solution[0], 1e-10); Assert.AreEqual(-284 / 109.0, target.Solution[1], 1e-10); Assert.AreEqual(-1024 / 109.0, target.Solution[2], 1e-10); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); }
public void GoldfarbIdnaniConstructorTest4() { // Solve the following optimization problem: // // min f(x) = 2x² - xy + 4y² - 5x - 6y // // s.t. x - y == 5 (x minus y should be equal to 5) // x >= 10 (x should be greater than or equal to 10) // var f = new QuadraticObjectiveFunction("2x² - xy + 4y² - 5x - 6y"); List<LinearConstraint> constraints = new List<LinearConstraint>(); constraints.Add(new LinearConstraint(f, "x-y = 5")); constraints.Add(new LinearConstraint(f, "x >= 10")); GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(2, constraints); double[,] A = { { 1, -1 }, { 1, 0 }, }; double[] b = { 5, 10, }; Assert.IsTrue(A.IsEqual(target.ConstraintMatrix)); Assert.IsTrue(b.IsEqual(target.ConstraintValues)); double[,] Q = { { +2*2, -1 }, { -1, +4*2 }, }; double[] d = { -5, -6 }; var actualQ = f.GetQuadraticTermsMatrix(); var actuald = f.GetLinearTermsVector(); Assert.IsTrue(Q.IsEqual(actualQ)); Assert.IsTrue(d.IsEqual(actuald)); }
private void button1_Click(object sender, EventArgs e) { String objectiveString = tbObjective.Text; String[] constraintStrings = tbConstraints.Lines; bool minimize = (string)comboBox1.SelectedItem == "min"; QuadraticObjectiveFunction function; LinearConstraint[] constraints = new LinearConstraint[constraintStrings.Length]; try { // Create objective function function = new QuadraticObjectiveFunction(objectiveString); } catch (FormatException) { tbSolution.Text = "Invalid objective function."; return; } // Create list of constraints for (int i = 0; i < constraints.Length; i++) { try { constraints[i] = new LinearConstraint(function, constraintStrings[i]); } catch (FormatException) { tbSolution.Text = "Invalid constraint at line " + i + "."; return; } } // Create solver var solver = new GoldfarbIdnaniQuadraticSolver(function.NumberOfVariables, constraints); try { // Solve the minimization or maximization problem double value = (minimize) ? solver.Minimize(function) : solver.Maximize(function); // Grab the solution found double[] solution = solver.Solution; // Format and display solution StringBuilder sb = new StringBuilder(); sb.AppendLine("Solution:"); sb.AppendLine(); sb.AppendLine(" " + objectiveString + " = " + value); sb.AppendLine(); for (int i = 0; i < solution.Length; i++) { string variableName = function.Indices[i]; sb.AppendLine(" " + variableName + " = " + solution[i]); } tbSolution.Text = sb.ToString(); } catch (NonPositiveDefiniteMatrixException) { tbSolution.Text = "Function is not positive definite."; } catch (ConvergenceException) { tbSolution.Text = "No possible solution could be attained."; } }
public void GoldfarbIdnaniMinimizeTest1() { // This test reproduces Issue #33 at Google Code Tracker // https://code.google.com/p/accord/issues/detail?id=33 // Create objective function using the // Hessian Q and linear terms vector d. double[,] Q = { { 0.12264004, 0.011579293, 0.103326825, 0.064073439 }, { 0.011579293, 0.033856, 0.014311947, 0.014732381 }, { 0.103326825, 0.014311947, 0.17715681, 0.067615114 }, { 0.064073439, 0.014732381, 0.067615114, 0.11539609 } }; Assert.IsTrue(Q.IsPositiveDefinite()); double[] d = { 0, 0, 0, 0 }; var f = new QuadraticObjectiveFunction(Q, d, "a", "b", "c", "d"); // Now, create the constraints var constraints = new LinearConstraintCollection(); constraints.Add(new LinearConstraint(f, "0.0732 * a + 0.0799 * b + 0.1926 * c + 0.0047 * d = 0.098")); constraints.Add(new LinearConstraint(f, "a + b + c + d = 1")); constraints.Add(new LinearConstraint(f, "a >= 0")); constraints.Add(new LinearConstraint(f, "b >= 0")); constraints.Add(new LinearConstraint(f, "c >= 0")); constraints.Add(new LinearConstraint(f, "d >= 0")); constraints.Add(new LinearConstraint(f, "a >= 0.5")); double[] b; int eq; double[,] A = constraints.CreateMatrix(4, out b, out eq); // Now we create the quadratic programming solver for 2 variables, using the constraints. GoldfarbIdnaniQuadraticSolver solver = new GoldfarbIdnaniQuadraticSolver(4, constraints); // And attempt to solve it. double minValue = solver.Minimize(f); double[] expected = { 0.5, 0.336259542, 0.163740458, 0 }; double[] actual = solver.Solution; for (int i = 0; i < expected.Length; i++) { double e = expected[i]; double a = actual[i]; Assert.AreEqual(e, a); } }
public void GoldfarbIdnaniConstructorTest1() { double[,] D = Matrix.Identity(3); double[] d = { 0, 5, 0 }; double[,] A = { { -4, -3, 0 }, { 2, 1, 0 }, { 0, -2, 1 }, }; double[] b = { -8, 2, 0 }; List<LinearConstraint> constraints = new List<LinearConstraint>(); constraints.Add(new LinearConstraint(-4, -3, +0) { Value = -8 }); constraints.Add(new LinearConstraint(+2, +1, +0) { Value = +2 }); constraints.Add(new LinearConstraint(+0, -2, +1) { Value = +0 }); GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(3, constraints); Assert.IsTrue(A.IsEqual(target.ConstraintMatrix)); Assert.IsTrue(b.IsEqual(target.ConstraintValues)); }
public void RunTest5() { // example from http://www.mail-archive.com/[email protected]/msg00831.html var cma = Matrix.Identity(10); var dva = new double[10]; double[,] Ama = { { 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { -1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 }, { 1, 1, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0 }, { -1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0 }, { -1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0 }, { -1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 }, { 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0 }, { -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 }, }; double[] bva = { 1, 1, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0 }; int meq = 2; GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(10, Ama.Transpose(), bva, meq); double value = target.Minimize(cma, dva.Multiply(-1)); for (int i = 0; i < target.Solution.Length; i += 2) { int N = target.Solution.Length / 2; Assert.AreEqual(1.0 / N, target.Solution[i], 1e-6); Assert.AreEqual(0.0, target.Solution[i + 1], 1e-6); } foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); }
public void RunTest4() { double[,] D = { { 5, -2, -1 }, { -2, 4, 3 }, { -1, 3, 5 }, }; double[] d = { -2, +35, +47 }; double[,] A = { { 0, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 }, }; double[] b = { 0, 0, 0 }; GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(3, A.Transpose(), b); double actual = target.Minimize(D, d.Multiply(-1)); Assert.AreEqual(-249.0, actual, 1e-6); Assert.IsFalse(double.IsNaN(actual)); Assert.AreEqual(1, target.Iterations); Assert.AreEqual(0, target.Deletions); Assert.AreEqual(3, target.Solution[0], 1e-6); Assert.AreEqual(5, target.Solution[1], 1e-6); Assert.AreEqual(7, target.Solution[2], 1e-6); Assert.AreEqual(0, target.Lagrangian[0]); Assert.AreEqual(0, target.Lagrangian[1]); Assert.AreEqual(0, target.Lagrangian[2]); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); }
public void RunTest3() { // Tested against R's QuadProg package /* solve.QP(matrix(c(10, -3, 1, -3, 11, -2, 1, -2, 12), 3, 3), c(1,5,3), t(matrix( c(-4, 2, 1, -3, 1, -2, 0, -1, 2), 3,3)), c(-8,4,-1)) */ double[,] D = { { 10, -3, 1 }, { -3, 11, -2 }, { 1, -2, 12 }, }; double[] d = { 1, 5, 3 }; double[,] A = { { -4, 2, 1 }, { -3, 1, -2 }, { 0, -1, 2 }, }; double[] b = { -8, 4, -1 }; GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(3, A.Transpose(), b); double actual = target.Minimize(D, d.Multiply(-1)); Assert.AreEqual(6.8, actual, 1e-5); Assert.IsFalse(double.IsNaN(actual)); Assert.AreEqual(4, target.Iterations); Assert.AreEqual(0, target.Deletions); Assert.AreEqual(+1.4, target.Solution[0], 1e-6); Assert.AreEqual(+0.8, target.Solution[1], 1e-6); Assert.AreEqual(-0.4, target.Solution[2], 1e-6); Assert.AreEqual(2.5333333, target.Lagrangian[0], 1e-6); Assert.AreEqual(9.7333333, target.Lagrangian[1], 1e-6); Assert.AreEqual(0.8666667, target.Lagrangian[2], 1e-6); Assert.AreEqual(1, target.ActiveConstraints[0]); Assert.AreEqual(0, target.ActiveConstraints[1]); Assert.AreEqual(2, target.ActiveConstraints[2]); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); }
public void RunTest2() { // Maximize f(x) = x² + 4y² -8x -16y // // s.t. x + y <= 5 // x <= 3 // x,y >= 0 // double[,] D = { { 2, 0 }, // 1x² { 0, 8 }, // 4y² }; double[] d = { -8, -16 }; double[,] A = { { 1, 1 }, // x + y { 1, 0 }, // x }; double[] b = { 5, 3 }; GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(2, A.Transpose(), b); double actual = target.Minimize(D, d.Multiply(-1)); Assert.AreEqual(64.8, actual); Assert.IsFalse(double.IsNaN(actual)); Assert.AreEqual(2, target.Iterations); Assert.AreEqual(0, target.Deletions); Assert.AreEqual(4.8, target.Solution[0], 1e-6); Assert.AreEqual(0.2, target.Solution[1], 1e-6); Assert.AreEqual(17.6, target.Lagrangian[0]); Assert.AreEqual(0.00, target.Lagrangian[1]); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); }
public void GoldfarbIdnaniConstructorTest9() { // Solve the following optimization problem: // // min f(x) = 2x² + xy + y² - 5y // // s.t. -x - 3y >= -2 // -x - y >= 0 // x >= 0 // y >= 0 // double x = 0, y = 0; var f = new QuadraticObjectiveFunction(() => 2 * (x * x) + (x * y) + (y * y) - 5 * y); List<LinearConstraint> constraints = new List<LinearConstraint>(); constraints.Add(new LinearConstraint(f, () => -x - 3 * y >= -2)); constraints.Add(new LinearConstraint(f, () => -x - y >= 0)); constraints.Add(new LinearConstraint(f, () => x >= 0)); constraints.Add(new LinearConstraint(f, () => y >= 0)); GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(2, constraints); double[,] expectedA = { { -1, -3 }, { -1, -1 }, { 1, 0 }, { 0, 1 }, }; double[] expectedb = { -2, 0, 0, 0 }; double[,] expectedQ = { { 4, 1 }, { 1, 2 }, }; double[] expectedd = { 0, -5 }; // Tested against R's QuadProg package /* Qmat = matrix(c(4,1,1,2),2,2) dvec = -c(0, -5) Amat = matrix(c(-1, -3, -1, -1, 1, 0, 0, 1), 2,4) bvec = c(-2, 0, 0, 0) solve.QP(Qmat, dvec, Amat, bvec) */ var actualA = target.ConstraintMatrix; var actualb = target.ConstraintValues; var actualQ = f.GetQuadraticTermsMatrix(); var actuald = f.GetLinearTermsVector(); Assert.IsTrue(expectedA.IsEqual(actualA)); Assert.IsTrue(expectedb.IsEqual(actualb)); Assert.IsTrue(expectedQ.IsEqual(actualQ)); Assert.IsTrue(expectedd.IsEqual(actuald)); double min = target.Minimize(f); double[] solution = target.Solution; Assert.AreEqual(0, solution[0], 1e-10); Assert.AreEqual(0, solution[1], 1e-10); Assert.AreEqual(0.0, min, 1e-10); Assert.AreEqual(0, target.Lagrangian[0], 1e-10); Assert.AreEqual(5, target.Lagrangian[1], 1e-10); Assert.AreEqual(5, target.Lagrangian[2], 1e-10); Assert.AreEqual(0, target.Lagrangian[3], 1e-10); Assert.IsFalse(Double.IsNaN(min)); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); }
public void GoldfarbIdnaniConstructorTest2() { // Solve the following optimization problem: // // min f(x) = 2x² - xy + 4y² - 5x - 6y // // s.t. x - y == 5 (x minus y should be equal to 5) // x >= 10 (x should be greater than or equal to 10) // // Lets first group the quadratic and linear terms. The // quadratic terms are +2x², +3y² and -4xy. The linear // terms are -2x and +1y. So our matrix of quadratic // terms can be expressed as: double[,] Q = // 2x² -1xy +4y² { /* x y */ /*x*/ { +2 /*xx*/ *2, -1 /*xy*/ }, /*y*/ { -1 /*xy*/ , +4 /*yy*/ *2 }, }; // Accordingly, our vector of linear terms is given by: double[] d = { -5 /*x*/, -6 /*y*/ }; // -5x -6y // We have now to express our constraints. We can do it // either by directly specifying a matrix A in which each // line refers to one of the constraints, expressing the // relationship between the different variables in the // constraint, like this: double[,] A = { { 1, -1 }, // This line says that x + (-y) ... (a) { 1, 0 }, // This line says that x alone ... (b) }; double[] b = { 5, // (a) ... should be equal to 5. 10, // (b) ... should be greater than or equal to 10. }; // Equalities must always come first, and in this case // we have to specify how many of the constraints are // actually equalities: int numberOfEqualities = 1; // Alternatively, we may use a more explicitly form: List<LinearConstraint> list = new List<LinearConstraint>(); // Define the first constraint, which involves only x list.Add(new LinearConstraint(numberOfVariables: 1) { // x is the first variable, thus located at // index 0. We are specifying that x >= 10: VariablesAtIndices = new[] { 0 }, // index 0 (x) ShouldBe = ConstraintType.GreaterThanOrEqualTo, Value = 10 }); // Define the second constraint, which involves x and y list.Add(new LinearConstraint(numberOfVariables: 2) { // x is the first variable, located at index 0, and y is // the second, thus located at 1. We are specifying that // x - y = 5 by saying that the variable at position 0 // times 1 plus the variable at position 1 times -1 // should be equal to 5. VariablesAtIndices = new int[] { 0, 1 }, // index 0 (x) and index 1 (y) CombinedAs = new double[] { 1, -1 }, // when combined as x - y ShouldBe = ConstraintType.EqualTo, Value = 5 }); // Now we can finally create our optimization problem var target = new GoldfarbIdnaniQuadraticSolver(numberOfVariables: 2, constraints: list); Assert.IsTrue(A.IsEqual(target.ConstraintMatrix)); Assert.IsTrue(b.IsEqual(target.ConstraintValues)); Assert.AreEqual(numberOfEqualities, target.NumberOfEqualities); // And attempt to solve it. double minimumValue = target.Minimize(Q, d); Assert.AreEqual(170, minimumValue, 1e-10); Assert.AreEqual(10, target.Solution[0]); Assert.AreEqual(05, target.Solution[1]); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); }
public void GoldfarbIdnaniMaximizeTest1() { // Solve the following optimization problem: // // max f(x) = -2x² + xy - y² + 5y // // s.t. x + y <= 0 // y >= 0 // // Create our objective function using a text string var f = new QuadraticObjectiveFunction("-2x² + xy - y² + 5y"); // Now, create the constraints List<LinearConstraint> constraints = new List<LinearConstraint>(); constraints.Add(new LinearConstraint(f, "x + y <= 0")); constraints.Add(new LinearConstraint(f, " y >= 0")); // Now we create the quadratic programming solver for 2 variables, using the constraints. GoldfarbIdnaniQuadraticSolver solver = new GoldfarbIdnaniQuadraticSolver(2, constraints); // And attempt to solve it. double maxValue = solver.Maximize(f); Assert.AreEqual(25 / 16.0, maxValue); Assert.AreEqual(-5 / 8.0, solver.Solution[0]); Assert.AreEqual(5 / 8.0, solver.Solution[1]); }
public void GoldfarbIdnaniConstructorTest3() { // Solve the following optimization problem: // // min f(x) = 2x² - xy + 4y² - 5x - 6y // // s.t. x - y == 5 (x minus y should be equal to 5) // x >= 10 (x should be greater than or equal to 10) // // In this example we will be using some symbolic processing. // The following variables could be initialized to any value. double x = 0, y = 0; // Create our objective function using a lambda expression var f = new QuadraticObjectiveFunction(() => 2 * (x * x) - (x * y) + 4 * (y * y) - 5 * x - 6 * y); // Now, create the constraints List<LinearConstraint> constraints = new List<LinearConstraint>(); constraints.Add(new LinearConstraint(f, () => x - y == 5)); constraints.Add(new LinearConstraint(f, () => x >= 10)); // Now we create the quadratic programming solver for 2 variables, using the constraints. GoldfarbIdnaniQuadraticSolver solver = new GoldfarbIdnaniQuadraticSolver(2, constraints); double[,] A = { { 1, -1 }, { 1, 0 }, }; double[] b = { 5, 10, }; Assert.IsTrue(A.IsEqual(solver.ConstraintMatrix)); Assert.IsTrue(b.IsEqual(solver.ConstraintValues)); double[,] Q = { { +2*2, -1 }, { -1, +4*2 }, }; double[] d = { -5, -6 }; var actualQ = f.GetQuadraticTermsMatrix(); var actuald = f.GetLinearTermsVector(); Assert.IsTrue(Q.IsEqual(actualQ)); Assert.IsTrue(d.IsEqual(actuald)); // And attempt to solve it. double minimumValue = solver.Minimize(f); }
public void RunTest1() { double[,] D = Matrix.Identity(3); double[] d = { 1, 5, 3 }; double[,] A = { { -4, 2, 0 }, { -3, 1, -2 }, { 0, 0, 1 }, }; double[] b = { -8, 2, 0 }; GoldfarbIdnaniQuadraticSolver target = new GoldfarbIdnaniQuadraticSolver(3, A.Transpose(), b); double actual = target.Minimize(D, d.Multiply(-1)); Assert.AreEqual(-12.41011, actual, 1e-5); Assert.IsFalse(double.IsNaN(actual)); Assert.AreEqual(3, target.Iterations); Assert.AreEqual(0, target.Deletions); Assert.AreEqual(0.4157303, target.Solution[0], 1e-6); Assert.AreEqual(2.1123596, target.Solution[1], 1e-6); Assert.AreEqual(4.2247191, target.Solution[2], 1e-6); Assert.AreEqual(0.1460674, target.Lagrangian[0], 1e-6); Assert.AreEqual(0.0000000, target.Lagrangian[1], 1e-6); Assert.AreEqual(1.2247191, target.Lagrangian[2], 1e-6); foreach (double v in target.Lagrangian) Assert.IsFalse(double.IsNaN(v)); foreach (double v in target.Solution) Assert.IsFalse(double.IsNaN(v)); }
/// <summary> /// Computes the optimization algorithm when the user /// presses the "Compute" button in the main interface. /// </summary> /// private void btnCompute_Click(object sender, EventArgs e) { // First, get what the user entered on screen: String strObjective = tbObjective.Text; String[] strConstraints = tbConstraints.Lines; // Check if this is a minimization or maximization task bool minimize = (string)comboBox1.SelectedItem == "min"; // Now we can start creating our function: QuadraticObjectiveFunction function; LinearConstraint[] constraints = new LinearConstraint[strConstraints.Length]; // Attempt to parse the string and create the objective function if (!QuadraticObjectiveFunction.TryParse(strObjective, out function)) { tbSolution.Text = "Invalid objective function."; return; } // Create list of constraints for (int i = 0; i < constraints.Length; i++) { if (!LinearConstraint.TryParse(strConstraints[i], function, out constraints[i])) { tbSolution.Text = "Invalid constraint at line " + i + "."; return; } } // Now, after the text has been parsed into actual objects, finally create the solver var solver = new GoldfarbIdnaniQuadraticSolver(function.NumberOfVariables, constraints); double optimumValue; try { // Solve the optimization problem: optimumValue = (minimize) ? solver.Minimize(function) : // the user wants to minimize it solver.Maximize(function); // the user wants to maximize it } catch (NonPositiveDefiniteMatrixException) { tbSolution.Text = "Function is not positive definite."; return; } catch (ConvergenceException) { tbSolution.Text = "No possible solution could be attained."; return; } // Retrieve the computed solution double[] solution = solver.Solution; // And let's format and display it: StringBuilder sb = new StringBuilder(); sb.AppendLine("Solution:"); sb.AppendLine(); sb.AppendLine(" " + strObjective + " = " + optimumValue); sb.AppendLine(); for (int i = 0; i < solution.Length; i++) { string variableName = function.Indices[i]; sb.AppendLine(" " + variableName + " = " + solution[i]); } tbSolution.Text = sb.ToString(); }