Constructor Detail

Method Detail

• solve

  @JvmOverloads() final static DoubleArray solve(Function1<DoubleArray, Double> objective, Integer nVars, Array<DoubleArray> Aeq, DoubleArray beq, Array<DoubleArray> Aub, DoubleArray bub, DoubleArray lb, DoubleArray ub, DoubleArray x0, Integer maxIter, Integer maxEval)

  Solve a linearly-constrained NLP problem using Augmented Lagrangian + BOBYQA.

  Minimizes objective(x) subject to: Aeq * x = beq (equality constraints) Aub * x <= bub (inequality constraints) lb <= x <= ub (variable bounds)

  Parameters:

  objective - The nonlinear objective function to minimize

  nVars - Number of decision variables

  Aeq - Equality constraint matrix (numEq x nVars), null if none

  beq - Equality RHS vector (numEq), null if none

  Aub - Inequality constraint matrix (numIneq x nVars), null if none

  bub - Inequality RHS vector (numIneq), null if none

  lb - Lower bounds on variables

  ub - Upper bounds on variables

  x0 - Initial point

  maxIter - Maximum outer iterations for augmented Lagrangian

  maxEval - Maximum function evaluations per BOBYQA call

  Returns:

  Optimal x vector

• solve

  @JvmOverloads() final static DoubleArray solve(Function1<DoubleArray, Double> objective, Integer nVars, Array<DoubleArray> Aeq, DoubleArray beq, Array<DoubleArray> Aub, DoubleArray bub, DoubleArray lb, DoubleArray ub, DoubleArray x0, Integer maxIter)

  Solve a linearly-constrained NLP problem using Augmented Lagrangian + BOBYQA.

  Minimizes objective(x) subject to: Aeq * x = beq (equality constraints) Aub * x <= bub (inequality constraints) lb <= x <= ub (variable bounds)

  Parameters:

  objective - The nonlinear objective function to minimize

  nVars - Number of decision variables

  Aeq - Equality constraint matrix (numEq x nVars), null if none

  beq - Equality RHS vector (numEq), null if none

  Aub - Inequality constraint matrix (numIneq x nVars), null if none

  bub - Inequality RHS vector (numIneq), null if none

  lb - Lower bounds on variables

  ub - Upper bounds on variables

  x0 - Initial point

  maxIter - Maximum outer iterations for augmented Lagrangian

  Returns:

  Optimal x vector

• solve

  @JvmOverloads() final static DoubleArray solve(Function1<DoubleArray, Double> objective, Integer nVars, Array<DoubleArray> Aeq, DoubleArray beq, Array<DoubleArray> Aub, DoubleArray bub, DoubleArray lb, DoubleArray ub, DoubleArray x0)

  Solve a linearly-constrained NLP problem using Augmented Lagrangian + BOBYQA.

  Minimizes objective(x) subject to: Aeq * x = beq (equality constraints) Aub * x <= bub (inequality constraints) lb <= x <= ub (variable bounds)

  Parameters:

  objective - The nonlinear objective function to minimize

  nVars - Number of decision variables

  Aeq - Equality constraint matrix (numEq x nVars), null if none

  beq - Equality RHS vector (numEq), null if none

  Aub - Inequality constraint matrix (numIneq x nVars), null if none

  bub - Inequality RHS vector (numIneq), null if none

  lb - Lower bounds on variables

  ub - Upper bounds on variables

  x0 - Initial point

  Returns:

  Optimal x vector

• solve

  @JvmOverloads() final static DoubleArray solve(Function1<DoubleArray, Double> objective, Integer nVars, Array<DoubleArray> Aeq, DoubleArray beq, Array<DoubleArray> Aub, DoubleArray lb, DoubleArray ub, DoubleArray x0)

  Solve a linearly-constrained NLP problem using Augmented Lagrangian + BOBYQA.

  Minimizes objective(x) subject to: Aeq * x = beq (equality constraints) Aub * x <= bub (inequality constraints) lb <= x <= ub (variable bounds)

  Parameters:

  objective - The nonlinear objective function to minimize

  nVars - Number of decision variables

  Aeq - Equality constraint matrix (numEq x nVars), null if none

  beq - Equality RHS vector (numEq), null if none

  Aub - Inequality constraint matrix (numIneq x nVars), null if none

  lb - Lower bounds on variables

  ub - Upper bounds on variables

  x0 - Initial point

  Returns:

  Optimal x vector

• solve

  @JvmOverloads() final static DoubleArray solve(Function1<DoubleArray, Double> objective, Integer nVars, Array<DoubleArray> Aeq, DoubleArray beq, DoubleArray lb, DoubleArray ub, DoubleArray x0)

  Solve a linearly-constrained NLP problem using Augmented Lagrangian + BOBYQA.

  Minimizes objective(x) subject to: Aeq * x = beq (equality constraints) Aub * x <= bub (inequality constraints) lb <= x <= ub (variable bounds)

  Parameters:

  objective - The nonlinear objective function to minimize

  nVars - Number of decision variables

  Aeq - Equality constraint matrix (numEq x nVars), null if none

  beq - Equality RHS vector (numEq), null if none

  lb - Lower bounds on variables

  ub - Upper bounds on variables

  x0 - Initial point

  Returns:

  Optimal x vector

• solve

  @JvmOverloads() final static DoubleArray solve(Function1<DoubleArray, Double> objective, Integer nVars, Array<DoubleArray> Aeq, DoubleArray lb, DoubleArray ub, DoubleArray x0)

  Solve a linearly-constrained NLP problem using Augmented Lagrangian + BOBYQA.

  Minimizes objective(x) subject to: Aeq * x = beq (equality constraints) Aub * x <= bub (inequality constraints) lb <= x <= ub (variable bounds)

  Parameters:

  objective - The nonlinear objective function to minimize

  nVars - Number of decision variables

  Aeq - Equality constraint matrix (numEq x nVars), null if none

  lb - Lower bounds on variables

  ub - Upper bounds on variables

  x0 - Initial point

  Returns:

  Optimal x vector

• solve

  @JvmOverloads() final static DoubleArray solve(Function1<DoubleArray, Double> objective, Integer nVars, DoubleArray lb, DoubleArray ub, DoubleArray x0)

  Solve a linearly-constrained NLP problem using Augmented Lagrangian + BOBYQA.

  Minimizes objective(x) subject to: Aeq * x = beq (equality constraints) Aub * x <= bub (inequality constraints) lb <= x <= ub (variable bounds)

  Parameters:

  objective - The nonlinear objective function to minimize

  nVars - Number of decision variables

  lb - Lower bounds on variables

  ub - Upper bounds on variables

  x0 - Initial point

  Returns:

  Optimal x vector