SQP算法实现不等式约束二次规划 java代码
时间: 2023-09-10 09:10:40 浏览: 50
以下是一个简单的Java实现,使用SQP算法解决不等式约束二次规划问题:
```java
import org.apache.commons.math3.analysis.MultivariateFunction;
import org.apache.commons.math3.optim.InitialGuess;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.PointValuePair;
import org.apache.commons.math3.optim.nonlinear.scalar.GoalType;
import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.AbstractScalarOptimizer;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplicialNelderMead;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimpleBounds;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplePointChecker;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SQP;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SQPUnconstrained;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SQPValidator;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.ScalarObjectiveFunction;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimpleValueChecker;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.ToleranceChecker;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.Weight;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.MultivariateJacobianFunction;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.NonLinearConjugateGradientOptimizer;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer.Decomposition;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer.Solver;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.optimization.PointValuePair;
import org.apache.commons.math3.optimization.direct.NelderMeadSimplex;
import org.apache.commons.math3.optimization.direct.SimplexOptimizer;
import org.apache.commons.math3.optimization.direct.PowellOptimizer;
import org.apache.commons.math3.optimization.direct.CMAESOptimizer;
import org.apache.commons.math3.optimization.direct.BOBYQAOptimizer;
import org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer;
import org.apache.commons.math3.optimization.direct.MultiDirectionalSimplex;
import org.apache.commons.math3.optimization.direct.AbstractSimplex;
import org.apache.commons.math3.optimization.direct.AbstractSimplexOptimizer;
import org.apache.commons.math3.optimization.direct.InitialGuess;
import org.apache.commons.math3.optimization.direct.SimpleBounds;
import org.apache.commons.math3.optimization.direct.SimpleValueChecker;
import org.apache.commons.math3.optimization.direct.SimplePointChecker;
import org.apache.commons.math3.optimization.direct.NelderMeadSimplex;
import org.apache.commons.math3.optimization.direct.MultiDirectionalSimplex;
import org.apache.commons.math3.optimization.direct.AbstractSimplex;
import org.apache.commons.math3.optimization.direct.DirectSearchOptimizer;
import org.apache.commons.math3.optimization.direct.SimplexOptimizer;
import org.apache.commons.math3.optimization.univariate.BrentOptimizer;
import org.apache.commons.math3.optimization.univariate.UnivariatePointValuePair;
public class SQPExample {
public static void main(String[] args) {
// Define the objective function
MultivariateFunction objective = new MultivariateFunction() {
public double value(double[] point) {
double x = point[0];
double y = point[1];
return x * x + y * y;
}
};
// Define the constraints
MultivariateFunction[] constraints = new MultivariateFunction[2];
constraints[0] = new MultivariateFunction() {
public double value(double[] point) {
double x = point[0];
double y = point[1];
return x - y + 1;
}
};
constraints[1] = new MultivariateFunction() {
public double value(double[] point) {
double x = point[0];
double y = point[1];
return x + y - 2;
}
};
// Define the initial guess
double[] initialGuess = {1, 1};
// Define the bounds
SimpleBounds bounds = new SimpleBounds(new double[]{-10, -10}, new double[]{10, 10});
// Define the optimizer
AbstractScalarOptimizer optimizer = new SQP(1e-5, new LevenbergMarquardtOptimizer(1e-10, 1e-10, 1e-10,
new Decomposition(Solver.SVD, 1e-10)), new SimpleValueChecker(1e-5, 1e-5));
// Define the objective function and constraints for the optimizer
ScalarObjectiveFunction objectiveFunction = new ScalarObjectiveFunction(objective);
MultivariateFunction[] constraintFunctions = constraints;
double[] constraintValues = {1, 2};
// Define the optimizer parameters
MaxEval maxEval = new MaxEval(1000);
InitialGuess initGuess = new InitialGuess(initialGuess);
SimpleBounds simpleBounds = bounds;
SQPUnconstrained opt1 = new SQPUnconstrained(optimizer, 1e-5, new ToleranceChecker(1e-5, 1e-5));
SQPValidator validator = new SQPValidator(new SimplePointChecker(1e-5, 1e-5));
// Solve the problem
PointValuePair solution = opt1.optimize(maxEval, objectiveFunction, simpleBounds, initGuess,
constraintFunctions, constraintValues, GoalType.MINIMIZE, validator);
// Print the solution
System.out.println("Solution: " + solution.getPoint()[0] + ", " + solution.getPoint()[1]);
System.out.println("Value: " + solution.getValue());
}
}
```
需要注意的是,这里使用了Apache Commons Math库来实现优化器和约束等功能。这个库可以在Maven中找到。
此代码仅供参考,实际应用中需要进行适当修改以适应具体问题。
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