序列二次规划算法 java代码
时间: 2023-10-22 07:14:51 浏览: 173
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:
```java
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。
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