序列二次规划算法 java代码

序列二次规划是一种优化算法，用于求解带约束的二次函数优化问题。以下是Java语言实现的序列二次规划算法代码：

import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.optim.ConvergenceChecker;
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.ObjectiveFunctionGradient;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer;

public class SQP {

    private final ConvergenceChecker<PointValuePair> checker;
    private final BOBYQAOptimizer optimizer;

    public SQP(int numInterpolationPoints, double initialTrustRegionRadius, double stoppingTrustRegionRadius) {
        this.checker = new PointValuePairChecker(stoppingTrustRegionRadius);
        this.optimizer = new BOBYQAOptimizer(numInterpolationPoints, initialTrustRegionRadius, stoppingTrustRegionRadius);
    }

    public PointValuePair optimize(ObjectiveFunction objective, ObjectiveFunctionGradient gradient, double[] startPoint, double[] lowerBound, double[] upperBound) {
        RealMatrix hessian = new Array2DRowRealMatrix(startPoint.length, startPoint.length);
        PointValuePair current = new PointValuePair(startPoint, objective.value(startPoint), true);
        PointValuePair previous = null;
        while (previous == null || checker.converged(1, previous, current)) {
            RealMatrix jacobian = new Array2DRowRealMatrix(gradient.value(current.getPoint()));
            PointValuePair next = optimizer.optimize(new ObjectiveFunction(QP.hessian(current.getPoint(), gradient, hessian)), new ObjectiveFunctionGradient(QP.jacobian(current.getPoint(), gradient, jacobian)), GoalType.MINIMIZE, QP.buildBounds(lowerBound, upperBound), QP.buildStartPoint(current.getPoint()));
            if (next.getValue() >= current.getValue()) {
                return current;
            }
            previous = current;
            current = next;
        }
        return current;
    }
}

这份代码依赖于Apache Commons Math库。其中，`optimize()`方法中的`objective`和`gradient`参数是目标函数和梯度函数，`startPoint`是初始点，`lowerBound`和`upperBound`是变量的下限和上限。函数返回最优解点和函数值。