Class ADMMQPConvergenceChecker

java.lang.Object
org.hipparchus.optim.nonlinear.vector.constrained.ADMMQPConvergenceChecker
All Implemented Interfaces:
ConvergenceChecker<LagrangeSolution>, OptimizationData

public class ADMMQPConvergenceChecker extends Object implements ConvergenceChecker<LagrangeSolution>, OptimizationData
Convergence Checker for ADMM QP Optimizer.
Since:
3.1
  • Method Details

    • converged

      public boolean converged(int i, LagrangeSolution previous, LagrangeSolution current)
      Check if the optimization algorithm has converged.
      Specified by:
      converged in interface ConvergenceChecker<LagrangeSolution>
      Parameters:
      i - Current iteration.
      previous - Best point in the previous iteration.
      current - Best point in the current iteration.
      Returns:
      true if the algorithm is considered to have converged.
    • converged

      public boolean converged(double rp, double rd, double maxPrimal, double maxDual)
      Evaluate convergence.
      Parameters:
      rp - primal residual
      rd - dual residual
      maxPrimal - primal vectors max
      maxDual - dual vectors max
      Returns:
      true of convergence has been reached
    • residualPrime

      public double residualPrime(RealVector x, RealVector z)
      Compute primal residual.
      Parameters:
      x - primal problem solution
      z - auxiliary variable
      Returns:
      primal residual
    • residualDual

      public double residualDual(RealVector x, RealVector y)
      Compute dual residual.
      Parameters:
      x - primal problem solution
      y - dual problem solution
      Returns:
      dual residual
    • maxPrimal

      public double maxPrimal(RealVector x, RealVector z)
      Compute primal vectors max.
      Parameters:
      x - primal problem solution
      z - auxiliary variable
      Returns:
      primal vectors max
    • maxDual

      public double maxDual(RealVector x, RealVector y)
      Compute dual vectors max.
      Parameters:
      x - primal problem solution
      y - dual problem solution
      Returns:
      dual vectors max