Uses of Class
de.labathome.trustconstr.matrix.Matrix

Packages that use Matrix

Package	Description
de.labathome.trustconstr
de.labathome.trustconstr.interfaces
de.labathome.trustconstr.matrix
de.labathome.trustconstr.records
de.labathome.trustconstr.sparse

Uses of Matrix in de.labathome.trustconstr

Fields in de.labathome.trustconstr declared as Matrix

Modifier and Type	Field	Description
protected final Matrix	LinearVectorFunction.A	Constraint matrix; mirrors scipy's self.J.
protected Matrix	LinearVectorFunction.f	Cached A x at LinearVectorFunction.x; null until first call.
protected final Matrix	LinearVectorFunction.H	Constant zero Hessian (n x n); shared across calls.
protected Matrix	LinearVectorFunction.v	Most recently set Lagrange multipliers (from LinearVectorFunction.hess(Matrix, Matrix)).
protected Matrix	LinearVectorFunction.x	Most recently evaluated point.

Methods in de.labathome.trustconstr that return Matrix

Modifier and Type	Method	Description
Matrix	CanonicalConstraint.HessianFn.apply(Matrix x, double[] vEq, double[] vIneq)
Matrix	FullHessianUpdateStrategy.apply(Matrix p, Object args)
Matrix	HessianLinearOperator.apply(Matrix x, Object args)
Matrix	LagrangianHessian.ConstraintHessian.apply(Matrix x, double[] vEq, double[] vIneq)
static Matrix	NumDiff.approxDerivative(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Finite-difference Jacobian of a vector-valued function with extra args.
static Matrix	NumDiff.approxDerivative(Function<Matrix,Matrix> f, Matrix x0, Matrix f0, FiniteDifferenceOptions options)	Finite-difference Jacobian of a vector-valued function.
static Matrix	NumDiff.approxDerivative(ToDoubleFunction<Matrix> f, Matrix x0, double f0, FiniteDifferenceOptions options)	Finite-difference gradient of a real-valued function of a vector argument.
static Matrix	NumDiff.approxDerivativeWithWorkers(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args, ExecutorService executor)	Strict scipy approx_derivative(..., workers=...) counterpart: computes a dense FD Jacobian column-by-column with the per-column evaluations dispatched in parallel through executor.
Matrix	CanonicalConstraint.InitialCanonical.cEq()	Returns the value of the cEq record component.
Matrix	CanonicalConstraint.InitialCanonical.cIneq()	Returns the value of the cIneq record component.
static Matrix	NumDiff.computeAbsoluteStep(Matrix relStep, Matrix x0, Matrix f0, FiniteDifferenceMethod method)	Compute the absolute finite-difference step from a relative step.
Matrix	CombinedConstraint.constrEq(Matrix x)
Matrix	LinearConstraint.constrEq(Matrix x)
Matrix	NonlinearConstraint.constrEq(Matrix x)
Matrix	SparsityForcedConstraint.constrEq(Matrix x)
Matrix	CombinedConstraint.constrIneq(Matrix x)
Matrix	LinearConstraint.constrIneq(Matrix x)
Matrix	NonlinearConstraint.constrIneq(Matrix x)
Matrix	SparsityForcedConstraint.constrIneq(Matrix x)
static Matrix[]	QPSubproblem.eqpKktFact(Matrix H, Matrix c, Matrix A, Matrix b)	Solve the equality-constrained quadratic program
Matrix	LinearVectorFunction.f()
Matrix	NonlinearVectorFunction.f()
Matrix	VectorFunction.f()
Matrix	LinearVectorFunction.fun(Matrix newX)
Matrix	NonlinearVectorFunction.fun(Matrix newX)
Matrix	VectorFunction.fun(Matrix x)
Matrix	ScalarFunction.g()	DANGER ZONE: returns the live internal gradient buffer; callers must not mutate it.
Matrix	LinearConstraint.getA()
Matrix	FullHessianUpdateStrategy.getMatrix()
Matrix	BarrierSubproblem.getScaling(Matrix z)	Returns the diagonal scaling matrix diag([1...1, s]) that decouples the variable rows from the slack rows in the trust-region step.
Matrix	BarrierSubproblem.getSlack(Matrix z)	Slice off the nIneq x 1 slack block from an augmented iterate z = [x; s].
Matrix	BarrierSubproblem.getVariables(Matrix z)	Slice off the nVars x 1 variable block from an augmented iterate z = [x; s].
Matrix	ScalarFunction.grad(Matrix x)
Matrix	ScalarFunction.H()	DANGER ZONE: returns the live internal Hessian buffer; callers must not mutate it.
Matrix	VectorFunction.H()
Matrix	CanonicalConstraint.hess(Matrix x, double[] vEq, double[] vIneq)	Evaluate the constraint Hessian-of-Lagrangian at x.
Matrix	LinearVectorFunction.hess(Matrix newX, Matrix newV)
Matrix	NonlinearVectorFunction.hess(Matrix newX, Matrix newV)
Matrix	ScalarFunction.hess(Matrix x)
Matrix	VectorFunction.hess(Matrix x, Matrix v)
Matrix	LinearVectorFunction.J()
Matrix	NonlinearVectorFunction.J()
Matrix	VectorFunction.J()
Matrix	LinearVectorFunction.jac(Matrix newX)
Matrix	NonlinearVectorFunction.jac(Matrix newX)
Matrix	VectorFunction.jac(Matrix x)
Matrix	CombinedConstraint.jacEq(Matrix x)
Matrix	LinearConstraint.jacEq(Matrix x)
Matrix	NonlinearConstraint.jacEq(Matrix x)
Matrix	SparsityForcedConstraint.jacEq(Matrix x)
Matrix	CombinedConstraint.jacIneq(Matrix x)
Matrix	LinearConstraint.jacIneq(Matrix x)
Matrix	NonlinearConstraint.jacIneq(Matrix x)
Matrix	SparsityForcedConstraint.jacIneq(Matrix x)
Matrix	CanonicalConstraint.InitialCanonical.JEq()	Returns the value of the JEq record component.
Matrix	CanonicalConstraint.InitialCanonical.JIneq()	Returns the value of the JIneq record component.
Matrix	CombinedConstraint.lagrangianContribution(Matrix x, double[] vEq, double[] vIneq)	Sum of constraint-Hessian-of-Lagrangian contributions across all sources.
Matrix	NonlinearConstraint.lagrangianContribution(Matrix x, double[] vEq, double[] vIneq)	Constraint Hessian-of-Lagrangian Sum_i v[i] * H_{c_i}(x), combining equality and inequality multipliers into a single Hessian contribution.
Matrix	SparsityForcedConstraint.lagrangianContribution(Matrix x, double[] vEq, double[] vIneq)	Pass-through to the source's Lagrangian-Hessian contribution.
Matrix	BarrierSubproblem.lagrHessS(Matrix z, Matrix v)	Scaled Lagrangian Hessian with respect to the slacks s, i.e.
Matrix	ScalarFunction.lowestX()
static Matrix	QPSubproblem.modifiedDogleg(Matrix A, Object Y, Matrix b, double trustRadius, double[] lb, double[] ub)	Approximately minimize 1/2 ||A x + b||^2 subject to ||x|| &lt; Delta and lb &le; x &le; ub using a modified dogleg approach.
Matrix	LinearVectorFunction.v()
Matrix	NonlinearVectorFunction.v()
Matrix	VectorFunction.v()

Methods in de.labathome.trustconstr that return types with arguments of type Matrix

Modifier and Type	Method	Description
Function<Matrix,Matrix>	NonlinearConstraint.userFun()
Function<Matrix,Matrix>	NonlinearConstraint.userFun()
BiFunction<Matrix,Matrix,Matrix>	NonlinearConstraint.userHess()
BiFunction<Matrix,Matrix,Matrix>	NonlinearConstraint.userHess()
BiFunction<Matrix,Matrix,Matrix>	NonlinearConstraint.userHess()
Function<Matrix,Matrix>	NonlinearConstraint.userJac()
Function<Matrix,Matrix>	NonlinearConstraint.userJac()

Methods in de.labathome.trustconstr with parameters of type Matrix

Modifier and Type	Method	Description
static AdjustedDifferencingScheme	NumDiff.adjustSchemeToBounds(Matrix x0, Matrix h, int numSteps, FiniteDifferenceMethod scheme, Matrix lb, Matrix ub)	Adjust finite-difference step sizes so that the perturbed point stays within [lb, ub] per dimension, flipping direction or switching forward / backward as needed.
Matrix	CanonicalConstraint.HessianFn.apply(Matrix x, double[] vEq, double[] vIneq)
Matrix	FullHessianUpdateStrategy.apply(Matrix p, Object args)
Matrix	HessianLinearOperator.apply(Matrix x, Object args)
LinearOperator	LagrangianHessian.apply(Matrix x, double[] vEq, double[] vIneq)
Matrix	LagrangianHessian.ConstraintHessian.apply(Matrix x, double[] vEq, double[] vIneq)
static Matrix	NumDiff.approxDerivative(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Finite-difference Jacobian of a vector-valued function with extra args.
static Matrix	NumDiff.approxDerivative(Function<Matrix,Matrix> f, Matrix x0, Matrix f0, FiniteDifferenceOptions options)	Finite-difference Jacobian of a vector-valued function.
static Matrix	NumDiff.approxDerivative(ToDoubleFunction<Matrix> f, Matrix x0, double f0, FiniteDifferenceOptions options)	Finite-difference gradient of a real-valued function of a vector argument.
static LinearOperator	NumDiff.approxDerivativeAsLinearOperator(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Strict scipy approx_derivative(..., as_linear_operator=True) counterpart: returns a matrix-free LinearOperator that applies the FD Jacobian J(x0) * p on demand.
static ApproxDerivativeResult	NumDiff.approxDerivativeFullOutput(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Strict scipy approx_derivative(..., full_output=True) counterpart: returns the FD Jacobian alongside an info dict carrying metadata about the computation.
static Matrix	NumDiff.approxDerivativeWithWorkers(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args, ExecutorService executor)	Strict scipy approx_derivative(..., workers=...) counterpart: computes a dense FD Jacobian column-by-column with the per-column evaluations dispatched in parallel through executor.
protected double	FullHessianUpdateStrategy.autoScale(Matrix deltaX, Matrix deltaG)	Heuristic scaling factor for the initial Hessian (or inverse-Hessian) approximation, based on Nocedal & Wright (2006), p.143 formula (6.20).
static Matrix	NumDiff.computeAbsoluteStep(Matrix relStep, Matrix x0, Matrix f0, FiniteDifferenceMethod method)	Compute the absolute finite-difference step from a relative step.
Matrix	CombinedConstraint.constrEq(Matrix x)
Matrix	LinearConstraint.constrEq(Matrix x)
Matrix	NonlinearConstraint.constrEq(Matrix x)
Matrix	SparsityForcedConstraint.constrEq(Matrix x)
Matrix	CombinedConstraint.constrIneq(Matrix x)
Matrix	LinearConstraint.constrIneq(Matrix x)
Matrix	NonlinearConstraint.constrIneq(Matrix x)
Matrix	SparsityForcedConstraint.constrIneq(Matrix x)
ScalarFunction.ScalarFunctionFactory	ScalarFunction.ScalarFunctionFactory.epsilon(Matrix epsilon)	Absolute step size for FD gradient/Hessian, taking precedence over the relative step.
static Matrix[]	QPSubproblem.eqpKktFact(Matrix H, Matrix c, Matrix A, Matrix b)	Solve the equality-constrained quadratic program
static StatefulResult	EqualityConstrainedSQP.eqSQP(IFunctionAndConstraint funAndConstr, IGradientAndJacobian gradAndJac, LagrangeHessian lagrHess, Matrix x0, double fun0, Matrix grad0, Matrix constr0, Matrix jac0, StoppingCriterion stopCrit, State state, double initialPenalty, double initialTrustRadius, ProjectionMethod factorizationMethod, Matrix trustLb, Matrix trustUb, LinearOperator scaling)	Solve a nonlinear equality-constrained problem using trust-region SQP.
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.finiteDiffJacSparsity(Matrix finiteDiffJacSparsity)	Specify a Jacobian sparsity pattern: a matrix with non-zero elements exactly where non-zero Jacobian entries are expected.
ScalarFunction.ScalarFunctionFactory	ScalarFunction.ScalarFunctionFactory.finiteDiffRelStep(Matrix finiteDiffRelStep)	Per-component relative step size for the FD gradient/Hessian.
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.finiteDiffRelStep(Matrix finiteDiffRelStep)	Per-component relative step size for the FD Jacobian/Hessian.
EqIneqSplit	CanonicalConstraint.fun(Matrix x)	Evaluate the canonical equality and inequality residuals at x.
Matrix	LinearVectorFunction.fun(Matrix newX)
Matrix	NonlinearVectorFunction.fun(Matrix newX)
double	ScalarFunction.fun(Matrix x)
Matrix	VectorFunction.fun(Matrix x)
FunctionAndConstraint	BarrierSubproblem.funAndConstr(Matrix z)	Evaluate the barrier function and the augmented constraints at a given augmented iterate z = [x; s]:
Matrix	BarrierSubproblem.getScaling(Matrix z)	Returns the diagonal scaling matrix diag([1...1, s]) that decouples the variable rows from the slack rows in the trust-region step.
Matrix	BarrierSubproblem.getSlack(Matrix z)	Slice off the nIneq x 1 slack block from an augmented iterate z = [x; s].
Matrix	BarrierSubproblem.getVariables(Matrix z)	Slice off the nVars x 1 variable block from an augmented iterate z = [x; s].
Matrix	ScalarFunction.grad(Matrix x)
GradientAndJacobian	BarrierSubproblem.gradAndJac(Matrix z)	Returns the scaled gradient and Jacobian for the augmented system:
static int[]	NumDiff.groupColumns(Matrix A)	Group columns of a 2-D matrix for sparse finite differencing.
static int[]	NumDiff.groupColumns(Matrix A, int[] order)	Group columns of a 2-D matrix for sparse FD with a user-supplied permutation that controls greedy-grouping order.
Matrix	CanonicalConstraint.hess(Matrix x, double[] vEq, double[] vIneq)	Evaluate the constraint Hessian-of-Lagrangian at x.
Matrix	LinearVectorFunction.hess(Matrix newX, Matrix newV)
Matrix	NonlinearVectorFunction.hess(Matrix newX, Matrix newV)
Matrix	ScalarFunction.hess(Matrix x)
Matrix	VectorFunction.hess(Matrix x, Matrix v)
static CanonicalConstraint.InitialCanonical	CanonicalConstraint.initialConstraintsAsCanonical(int n, List<CanonicalConstraint> canonicals, Matrix x0, boolean sparseJacobian)	Stack the canonical residuals and Jacobians of every constraint in canonicals at the starting iterate x0.
EqIneqSplit	CanonicalConstraint.jac(Matrix x)	Evaluate the canonical equality and inequality Jacobians at x.
Matrix	LinearVectorFunction.jac(Matrix newX)
Matrix	NonlinearVectorFunction.jac(Matrix newX)
Matrix	VectorFunction.jac(Matrix x)
Matrix	CombinedConstraint.jacEq(Matrix x)
Matrix	LinearConstraint.jacEq(Matrix x)
Matrix	NonlinearConstraint.jacEq(Matrix x)
Matrix	SparsityForcedConstraint.jacEq(Matrix x)
Matrix	CombinedConstraint.jacIneq(Matrix x)
Matrix	LinearConstraint.jacIneq(Matrix x)
Matrix	NonlinearConstraint.jacIneq(Matrix x)
Matrix	SparsityForcedConstraint.jacIneq(Matrix x)
Matrix	CombinedConstraint.lagrangianContribution(Matrix x, double[] vEq, double[] vIneq)	Sum of constraint-Hessian-of-Lagrangian contributions across all sources.
Matrix	NonlinearConstraint.lagrangianContribution(Matrix x, double[] vEq, double[] vIneq)	Constraint Hessian-of-Lagrangian Sum_i v[i] * H_{c_i}(x), combining equality and inequality multipliers into a single Hessian contribution.
Matrix	SparsityForcedConstraint.lagrangianContribution(Matrix x, double[] vEq, double[] vIneq)	Pass-through to the source's Lagrangian-Hessian contribution.
LinearOperator	BarrierSubproblem.lagrangianHessian(Matrix z, Matrix v)	Full augmented Lagrangian Hessian, block-diagonal:
Matrix	BarrierSubproblem.lagrHessS(Matrix z, Matrix v)	Scaled Lagrangian Hessian with respect to the slacks s, i.e.
LinearOperator	BarrierSubproblem.lagrHessX(Matrix z, Matrix v)	Lagrangian Hessian with respect to x (the Hx block).
static LinearOperator	NumDiff.linearOperatorDifference(Function<Matrix,Matrix> fun, Matrix x0, Matrix f0, Matrix relStep, FiniteDifferenceMethod method)	Build a LinearOperator that applies the FD Jacobian J(x0)*p on demand, without materialising the full Jacobian.
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Matrix x0, C constraint, int maxIter, double xtol, double gtol)	Most general convenience overload: only the objective function is supplied.
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, HessianUpdateStrategy strategy, Matrix x0, C constraint, int maxIter, double xtol, double gtol)	Convenience overload accepting any HessianUpdateStrategy (BFGS, SR1, custom).
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, Matrix x0, C constraint, int maxIter, double xtol, double gtol)	Convenience overload that approximates the objective Hessian via a fresh BFGS update strategy -- the scipy-default behaviour when hess is omitted from the user call.
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, Function<Matrix,Matrix> hess, Matrix x0, C constraint, int maxIter, double xtol, double gtol)	General-purpose entry point: routes to MinimizeTrustConstr.minimizeEqualityConstrained(java.util.function.Function<de.labathome.trustconstr.matrix.Matrix, java.lang.Double>, java.util.function.Function<de.labathome.trustconstr.matrix.Matrix, de.labathome.trustconstr.matrix.Matrix>, java.util.function.Function<de.labathome.trustconstr.matrix.Matrix, de.labathome.trustconstr.matrix.Matrix>, de.labathome.trustconstr.matrix.Matrix, C, int, double, double) when the constraint has only equality rows, and to a TrustRegionInteriorPoint-driven path when any inequality rows are present.
static OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, Function<Matrix,Matrix> hess, Matrix x0, Object[] constraints, int maxIter, double xtol, double gtol)	Multi-constraint entry point: combines the supplied constraints into a CombinedConstraint and dispatches as in the single-constraint overload.
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimizeEqualityConstrained(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, Function<Matrix,Matrix> hess, Matrix x0, C eq, int maxIter, double xtol, double gtol)	Convenience entry point covering the equality-constrained slice of the trust-constr API: a scalar objective with an explicit gradient and Hessian, subject to optional pure-equality constraints from a single LinearConstraint or NonlinearConstraint.
static OptimizeResult	MinimizeTrustConstr.minimizeTrustConstr(ToDoubleBiFunction<Matrix,Object> fun, Matrix x0, Object args, BiFunction<Matrix,Object,Matrix> grad, BiFunction<Matrix,Object,Matrix> hess, HessianProduct hessp, Bounds bounds, Object constraints, double xTol, double gTol, double barrierTol, Optional<Boolean> sparseJacobian, IterationCallback callback, int maxIter, int verbose, Matrix finiteDifferenceRelStep, double initialConstraintPenalty, double initialTrustRadius, double initialBarrierParameter, double initialBarrierTolerance, ProjectionMethod factorizationMethod, boolean disp)	Minimize a scalar function subject to constraints.
static OptimizeResult	MinimizeTrustConstr.minimizeTrustConstr(ToDoubleBiFunction<Matrix,Object> fun, Matrix x0, Object args, BiFunction<Matrix,Object,Matrix> grad, BiFunction<Matrix,Object,Matrix> hess, HessianProduct hessp, Bounds bounds, Object constraints, double xTol, double gTol, double barrierTol, Optional<Boolean> sparseJacobian, IterationCallback callback, int maxIter, int verbose, Matrix finiteDifferenceRelStep, double initialConstraintPenalty, double initialTrustRadius, double initialBarrierParameter, double initialBarrierTolerance, ProjectionMethod factorizationMethod, boolean disp, ExecutorService workers)	Strict scipy _minimize_trustregion_constr(..., workers=N) counterpart.
static Matrix	QPSubproblem.modifiedDogleg(Matrix A, Object Y, Matrix b, double trustRadius, double[] lb, double[] ub)	Approximately minimize 1/2 ||A x + b||^2 subject to ||x|| &lt; Delta and lb &le; x &le; ub using a modified dogleg approach.
static double	Projections.orthogonality(Matrix A, Matrix g)	Measure orthogonality between a vector and the null space of a matrix.
static CGInfo	QPSubproblem.projectedCG(LinearOperator H, Matrix c, Object Z, Object Y, Matrix b)	Solve the equality-constrained quadratic program
static CGInfo	QPSubproblem.projectedCG(LinearOperator H, Matrix c, Object Z, Object Y, Matrix b, double trustRadius, double[] lb, double[] ub, double tolerance)	Solve EQP problem with projected CG method.
static CGInfo	QPSubproblem.projectedCG(LinearOperator H, Matrix c, Object Z, Object Y, Matrix b, double trustRadius, double[] lb, double[] ub, double tolerance, long maxIterations, long maxInfeasibleIterations, boolean returnAll)	Solve the equality-constrained quadratic program
static LinearOperator[]	Projections.projections(Matrix A)	Convenience overload: auto-select factorisation method, defaults for orthogonality / refinement / numerical-zero tolerances.
static LinearOperator[]	Projections.projections(Matrix A, ProjectionMethod method)	Convenience overload with an explicit factorisation method.
static LinearOperator[]	Projections.projections(Matrix A, ProjectionMethod method, double orthTol)	Convenience overload exposing the orthogonality tolerance.
static LinearOperator[]	Projections.projections(Matrix A, ProjectionMethod method, double orthTol, int maxRefine)	Convenience overload exposing the maximum number of refinement iterations.
static LinearOperator[]	Projections.projections(Matrix A, ProjectionMethod method, double orthTol, int maxRefine, double tolerance)	Return three linear operators related with a given matrix A.
Residual	LinearConstraint.residual(Matrix x)	Lower and upper residuals at x: sl = A x - lb (positive iff lower bound satisfied), sb = ub - A x (positive iff upper bound satisfied).
boolean	BarrierSubproblem.stoppingCriteria(State state, Matrix z, boolean lastIterationFailed, double optimality, double constrViolation, double trustRadius, double penalty, CGInfo cgInfo)	Stopping criterion for the barrier subproblem; mirrors formula (2.3) from Byrd-Hribar-Nocedal (1999), p.879.
static StatefulResult	TrustRegionInteriorPoint.trustRegionInteriorPoint(ToDoubleBiFunction<Matrix,Object> fun, BiFunction<Matrix,Object,Matrix> grad, LagrangeHessian lagrHess, int nVars, int nIneq, int nEq, Constraint constr, Jacobian jac, Matrix x0, double fun0, Matrix grad0, Matrix constrIneq0, Matrix jacIneq0, Matrix constrEq0, Matrix jacEq0, GlobalStoppingCriteria stopCrit, boolean[] enforceFeasibility, double xtol, State state, double initialBarrierParameter, double initialTolerance, double initialPenalty, double initialTrustRadius, ProjectionMethod factorizationMethod)	Trust-region interior-point solve.
void	FullHessianUpdateStrategy.update(Matrix deltaX, Matrix deltaG)
static State	MinimizeTrustConstr.updateState(State state, Matrix x, boolean lastIterationFailed, ScalarFunction objective, PreparedConstraint[] preparedConstraints, long startTime, double trustRadius, double constraintPenalty, CGInfo cgInfo)	Refresh the equality-path State record after an outer iteration (the function value, gradient, optimality, constraint violation, trust-region and CG fields).
static StateIP	MinimizeTrustConstr.updateStateIP(StateIP state, Matrix x, boolean lastIterationFailed, ScalarFunction objective, PreparedConstraint[] preparedConstraints, long startTime, double trustRadius, double constraintPenalty, CGInfo cgInfo, double barrierParameter, double barrierTolerance)	Refresh the IP-path StateIP record after an outer iteration: delegates to MinimizeTrustConstr.updateState(de.labathome.trustconstr.records.State, de.labathome.trustconstr.matrix.Matrix, boolean, de.labathome.trustconstr.ScalarFunction, de.labathome.trustconstr.records.PreparedConstraint[], long, double, double, de.labathome.trustconstr.records.CGInfo) and then sets the barrier-specific fields.
void	CombinedConstraint.validateKeepFeasibleAtStart(Matrix x0)	Walk all sources and validate that any row marked keep_feasible=True is satisfied at x0.
void	LinearConstraint.validateKeepFeasibleAtStart(Matrix x0)	Throws IllegalArgumentException if any row marked keepFeasible[i] == true is violated at the supplied starting point.
void	NonlinearConstraint.validateKeepFeasibleAtStart(Matrix x0)	Throws IllegalArgumentException if any row marked keepFeasible[i] == true is violated at the supplied starting point.
void	SparsityForcedConstraint.validateKeepFeasibleAtStart(Matrix x0)	Pass-through to the source's keep-feasible-at-start validation.
ScalarFunction.ScalarFunctionFactory	ScalarFunction.ScalarFunctionFactory.x0(Matrix x0)	Provide the initial point for evaluating fun.
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.x0(Matrix x0)	Provide the initial point.

Method parameters in de.labathome.trustconstr with type arguments of type Matrix

Modifier and Type	Method	Description
static Matrix	NumDiff.approxDerivative(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Finite-difference Jacobian of a vector-valued function with extra args.
static Matrix	NumDiff.approxDerivative(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Finite-difference Jacobian of a vector-valued function with extra args.
static Matrix	NumDiff.approxDerivative(Function<Matrix,Matrix> f, Matrix x0, Matrix f0, FiniteDifferenceOptions options)	Finite-difference Jacobian of a vector-valued function.
static Matrix	NumDiff.approxDerivative(Function<Matrix,Matrix> f, Matrix x0, Matrix f0, FiniteDifferenceOptions options)	Finite-difference Jacobian of a vector-valued function.
static double	NumDiff.approxDerivative(ToDoubleFunction<Matrix> f, double x0, double f0, FiniteDifferenceOptions options)	Finite-difference gradient of a scalar function of a scalar argument with a precomputed f0.
static double	NumDiff.approxDerivative(ToDoubleFunction<Matrix> f, double x0, FiniteDifferenceOptions options)	Finite-difference gradient of a ToDoubleFunction of a scalar argument.
static Matrix	NumDiff.approxDerivative(ToDoubleFunction<Matrix> f, Matrix x0, double f0, FiniteDifferenceOptions options)	Finite-difference gradient of a real-valued function of a vector argument.
static LinearOperator	NumDiff.approxDerivativeAsLinearOperator(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Strict scipy approx_derivative(..., as_linear_operator=True) counterpart: returns a matrix-free LinearOperator that applies the FD Jacobian J(x0) * p on demand.
static LinearOperator	NumDiff.approxDerivativeAsLinearOperator(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Strict scipy approx_derivative(..., as_linear_operator=True) counterpart: returns a matrix-free LinearOperator that applies the FD Jacobian J(x0) * p on demand.
static ApproxDerivativeResult	NumDiff.approxDerivativeFullOutput(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Strict scipy approx_derivative(..., full_output=True) counterpart: returns the FD Jacobian alongside an info dict carrying metadata about the computation.
static ApproxDerivativeResult	NumDiff.approxDerivativeFullOutput(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args)	Strict scipy approx_derivative(..., full_output=True) counterpart: returns the FD Jacobian alongside an info dict carrying metadata about the computation.
static Matrix	NumDiff.approxDerivativeWithWorkers(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args, ExecutorService executor)	Strict scipy approx_derivative(..., workers=...) counterpart: computes a dense FD Jacobian column-by-column with the per-column evaluations dispatched in parallel through executor.
static Matrix	NumDiff.approxDerivativeWithWorkers(BiFunction<Matrix,Object,Matrix> fun, Matrix x0, Matrix f0, FiniteDifferenceOptions options, Object args, ExecutorService executor)	Strict scipy approx_derivative(..., workers=...) counterpart: computes a dense FD Jacobian column-by-column with the per-column evaluations dispatched in parallel through executor.
ScalarFunction.ScalarFunctionFactory	ScalarFunction.ScalarFunctionFactory.fun(ToDoubleBiFunction<Matrix,Object> fun)	Set the objective function to optimize.
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.fun(Function<Matrix,Matrix> fun)	Set the vector-valued function to evaluate.
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.fun(Function<Matrix,Matrix> fun)	Set the vector-valued function to evaluate.
ScalarFunction.ScalarFunctionFactory	ScalarFunction.ScalarFunctionFactory.grad(BiFunction<Matrix,Object,Matrix> grad)	Provide an analytic gradient grad(x, args) -> gradf(x).
ScalarFunction.ScalarFunctionFactory	ScalarFunction.ScalarFunctionFactory.grad(BiFunction<Matrix,Object,Matrix> grad)	Provide an analytic gradient grad(x, args) -> gradf(x).
ScalarFunction.ScalarFunctionFactory	ScalarFunction.ScalarFunctionFactory.hess(BiFunction<Matrix,Object,Matrix> hess)	Provide an analytic Hessian hess(x, args) -> grad^2f(x).
ScalarFunction.ScalarFunctionFactory	ScalarFunction.ScalarFunctionFactory.hess(BiFunction<Matrix,Object,Matrix> hess)	Provide an analytic Hessian hess(x, args) -> grad^2f(x).
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.hess(BiFunction<Matrix,Matrix,Matrix> hess)	Provide an analytic constraint Hessian-of-Lagrangian (x, v) -> Sum v[i] H_{c_i}(x).
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.hess(BiFunction<Matrix,Matrix,Matrix> hess)	Provide an analytic constraint Hessian-of-Lagrangian (x, v) -> Sum v[i] H_{c_i}(x).
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.hess(BiFunction<Matrix,Matrix,Matrix> hess)	Provide an analytic constraint Hessian-of-Lagrangian (x, v) -> Sum v[i] H_{c_i}(x).
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.jac(Function<Matrix,Matrix> jac)	Provide an analytic Jacobian.
VectorFunction.VectorFunctionFactory	VectorFunction.VectorFunctionFactory.jac(Function<Matrix,Matrix> jac)	Provide an analytic Jacobian.
static LinearOperator	NumDiff.linearOperatorDifference(Function<Matrix,Matrix> fun, Matrix x0, Matrix f0, Matrix relStep, FiniteDifferenceMethod method)	Build a LinearOperator that applies the FD Jacobian J(x0)*p on demand, without materialising the full Jacobian.
static LinearOperator	NumDiff.linearOperatorDifference(Function<Matrix,Matrix> fun, Matrix x0, Matrix f0, Matrix relStep, FiniteDifferenceMethod method)	Build a LinearOperator that applies the FD Jacobian J(x0)*p on demand, without materialising the full Jacobian.
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Matrix x0, C constraint, int maxIter, double xtol, double gtol)	Most general convenience overload: only the objective function is supplied.
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, HessianUpdateStrategy strategy, Matrix x0, C constraint, int maxIter, double xtol, double gtol)	Convenience overload accepting any HessianUpdateStrategy (BFGS, SR1, custom).
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, Matrix x0, C constraint, int maxIter, double xtol, double gtol)	Convenience overload that approximates the objective Hessian via a fresh BFGS update strategy -- the scipy-default behaviour when hess is omitted from the user call.
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, Function<Matrix,Matrix> hess, Matrix x0, C constraint, int maxIter, double xtol, double gtol)	General-purpose entry point: routes to MinimizeTrustConstr.minimizeEqualityConstrained(java.util.function.Function<de.labathome.trustconstr.matrix.Matrix, java.lang.Double>, java.util.function.Function<de.labathome.trustconstr.matrix.Matrix, de.labathome.trustconstr.matrix.Matrix>, java.util.function.Function<de.labathome.trustconstr.matrix.Matrix, de.labathome.trustconstr.matrix.Matrix>, de.labathome.trustconstr.matrix.Matrix, C, int, double, double) when the constraint has only equality rows, and to a TrustRegionInteriorPoint-driven path when any inequality rows are present.
static OptimizeResult	MinimizeTrustConstr.minimize(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, Function<Matrix,Matrix> hess, Matrix x0, Object[] constraints, int maxIter, double xtol, double gtol)	Multi-constraint entry point: combines the supplied constraints into a CombinedConstraint and dispatches as in the single-constraint overload.
static <C extends Constraint & Jacobian> OptimizeResult	MinimizeTrustConstr.minimizeEqualityConstrained(Function<Matrix,Double> fun, Function<Matrix,Matrix> grad, Function<Matrix,Matrix> hess, Matrix x0, C eq, int maxIter, double xtol, double gtol)	Convenience entry point covering the equality-constrained slice of the trust-constr API: a scalar objective with an explicit gradient and Hessian, subject to optional pure-equality constraints from a single LinearConstraint or NonlinearConstraint.
static OptimizeResult	MinimizeTrustConstr.minimizeTrustConstr(ToDoubleBiFunction<Matrix,Object> fun, Matrix x0, Object args, BiFunction<Matrix,Object,Matrix> grad, BiFunction<Matrix,Object,Matrix> hess, HessianProduct hessp, Bounds bounds, Object constraints, double xTol, double gTol, double barrierTol, Optional<Boolean> sparseJacobian, IterationCallback callback, int maxIter, int verbose, Matrix finiteDifferenceRelStep, double initialConstraintPenalty, double initialTrustRadius, double initialBarrierParameter, double initialBarrierTolerance, ProjectionMethod factorizationMethod, boolean disp)	Minimize a scalar function subject to constraints.
static OptimizeResult	MinimizeTrustConstr.minimizeTrustConstr(ToDoubleBiFunction<Matrix,Object> fun, Matrix x0, Object args, BiFunction<Matrix,Object,Matrix> grad, BiFunction<Matrix,Object,Matrix> hess, HessianProduct hessp, Bounds bounds, Object constraints, double xTol, double gTol, double barrierTol, Optional<Boolean> sparseJacobian, IterationCallback callback, int maxIter, int verbose, Matrix finiteDifferenceRelStep, double initialConstraintPenalty, double initialTrustRadius, double initialBarrierParameter, double initialBarrierTolerance, ProjectionMethod factorizationMethod, boolean disp)	Minimize a scalar function subject to constraints.
static OptimizeResult	MinimizeTrustConstr.minimizeTrustConstr(ToDoubleBiFunction<Matrix,Object> fun, Matrix x0, Object args, BiFunction<Matrix,Object,Matrix> grad, BiFunction<Matrix,Object,Matrix> hess, HessianProduct hessp, Bounds bounds, Object constraints, double xTol, double gTol, double barrierTol, Optional<Boolean> sparseJacobian, IterationCallback callback, int maxIter, int verbose, Matrix finiteDifferenceRelStep, double initialConstraintPenalty, double initialTrustRadius, double initialBarrierParameter, double initialBarrierTolerance, ProjectionMethod factorizationMethod, boolean disp)	Minimize a scalar function subject to constraints.
static OptimizeResult	MinimizeTrustConstr.minimizeTrustConstr(ToDoubleBiFunction<Matrix,Object> fun, Matrix x0, Object args, BiFunction<Matrix,Object,Matrix> grad, BiFunction<Matrix,Object,Matrix> hess, HessianProduct hessp, Bounds bounds, Object constraints, double xTol, double gTol, double barrierTol, Optional<Boolean> sparseJacobian, IterationCallback callback, int maxIter, int verbose, Matrix finiteDifferenceRelStep, double initialConstraintPenalty, double initialTrustRadius, double initialBarrierParameter, double initialBarrierTolerance, ProjectionMethod factorizationMethod, boolean disp, ExecutorService workers)	Strict scipy _minimize_trustregion_constr(..., workers=N) counterpart.
static OptimizeResult	MinimizeTrustConstr.minimizeTrustConstr(ToDoubleBiFunction<Matrix,Object> fun, Matrix x0, Object args, BiFunction<Matrix,Object,Matrix> grad, BiFunction<Matrix,Object,Matrix> hess, HessianProduct hessp, Bounds bounds, Object constraints, double xTol, double gTol, double barrierTol, Optional<Boolean> sparseJacobian, IterationCallback callback, int maxIter, int verbose, Matrix finiteDifferenceRelStep, double initialConstraintPenalty, double initialTrustRadius, double initialBarrierParameter, double initialBarrierTolerance, ProjectionMethod factorizationMethod, boolean disp, ExecutorService workers)	Strict scipy _minimize_trustregion_constr(..., workers=N) counterpart.
static OptimizeResult	MinimizeTrustConstr.minimizeTrustConstr(ToDoubleBiFunction<Matrix,Object> fun, Matrix x0, Object args, BiFunction<Matrix,Object,Matrix> grad, BiFunction<Matrix,Object,Matrix> hess, HessianProduct hessp, Bounds bounds, Object constraints, double xTol, double gTol, double barrierTol, Optional<Boolean> sparseJacobian, IterationCallback callback, int maxIter, int verbose, Matrix finiteDifferenceRelStep, double initialConstraintPenalty, double initialTrustRadius, double initialBarrierParameter, double initialBarrierTolerance, ProjectionMethod factorizationMethod, boolean disp, ExecutorService workers)	Strict scipy _minimize_trustregion_constr(..., workers=N) counterpart.
static StatefulResult	TrustRegionInteriorPoint.trustRegionInteriorPoint(ToDoubleBiFunction<Matrix,Object> fun, BiFunction<Matrix,Object,Matrix> grad, LagrangeHessian lagrHess, int nVars, int nIneq, int nEq, Constraint constr, Jacobian jac, Matrix x0, double fun0, Matrix grad0, Matrix constrIneq0, Matrix jacIneq0, Matrix constrEq0, Matrix jacEq0, GlobalStoppingCriteria stopCrit, boolean[] enforceFeasibility, double xtol, State state, double initialBarrierParameter, double initialTolerance, double initialPenalty, double initialTrustRadius, ProjectionMethod factorizationMethod)	Trust-region interior-point solve.
static StatefulResult	TrustRegionInteriorPoint.trustRegionInteriorPoint(ToDoubleBiFunction<Matrix,Object> fun, BiFunction<Matrix,Object,Matrix> grad, LagrangeHessian lagrHess, int nVars, int nIneq, int nEq, Constraint constr, Jacobian jac, Matrix x0, double fun0, Matrix grad0, Matrix constrIneq0, Matrix jacIneq0, Matrix constrEq0, Matrix jacEq0, GlobalStoppingCriteria stopCrit, boolean[] enforceFeasibility, double xtol, State state, double initialBarrierParameter, double initialTolerance, double initialPenalty, double initialTrustRadius, ProjectionMethod factorizationMethod)	Trust-region interior-point solve.
static StatefulResult	TrustRegionInteriorPoint.trustRegionInteriorPoint(ToDoubleBiFunction<Matrix,Object> fun, BiFunction<Matrix,Object,Matrix> grad, LagrangeHessian lagrHess, int nVars, int nIneq, int nEq, Constraint constr, Jacobian jac, Matrix x0, double fun0, Matrix grad0, Matrix constrIneq0, Matrix jacIneq0, Matrix constrEq0, Matrix jacEq0, GlobalStoppingCriteria stopCrit, boolean[] enforceFeasibility, double xtol, State state, double initialBarrierParameter, double initialTolerance, double initialPenalty, double initialTrustRadius, ProjectionMethod factorizationMethod)	Trust-region interior-point solve.

Constructors in de.labathome.trustconstr with parameters of type Matrix

Modifier	Constructor	Description
	BarrierSubproblem(Matrix x0, Matrix s0, ToDoubleBiFunction<Matrix,Object> fun, BiFunction<Matrix,Object,Matrix> grad, LagrangeHessian lagrHess, long nVars, long nIneq, long nEq, Constraint constr, Jacobian jac, double barrierParameter, double tolerance, boolean[] enforceFeasibility, GlobalStoppingCriteria globalStopCriteria, double xtol, double fun0, Matrix grad0, Matrix constrIneq0, Matrix jacIneq0, Matrix constrEq0, Matrix jacEq0)	Construct the barrier subproblem at a starting iterate.
	IdentityVectorFunction(Matrix x0, Optional<Boolean> sparseJacobian)	Build an identity vector function sized for the iterate x0.
	InitialCanonical(Matrix cEq, Matrix cIneq, Matrix JEq, Matrix JIneq)	Creates an instance of a InitialCanonical record class.
	LinearConstraint(Matrix a, double[] lb, double[] ub)	Convenience overload: equivalent to LinearConstraint(Matrix, double[], double[], boolean[]) with keepFeasible = null.
	LinearConstraint(Matrix a, double[] lb, double[] ub, boolean[] keepFeasible)	Build a linear constraint lb &le; A x &le; ub.
	LinearVectorFunction(Matrix A, Matrix x0, Optional<Boolean> sparseJacobian)	Build a linear vector function f(x) = A x.
	NonlinearVectorFunction(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, Matrix x0, Optional<Boolean> sparseJacobian)	Build a nonlinear vector function and cache f, J at x0.

Constructor parameters in de.labathome.trustconstr with type arguments of type Matrix

Modifier	Constructor	Description
	BarrierSubproblem(Matrix x0, Matrix s0, ToDoubleBiFunction<Matrix,Object> fun, BiFunction<Matrix,Object,Matrix> grad, LagrangeHessian lagrHess, long nVars, long nIneq, long nEq, Constraint constr, Jacobian jac, double barrierParameter, double tolerance, boolean[] enforceFeasibility, GlobalStoppingCriteria globalStopCriteria, double xtol, double fun0, Matrix grad0, Matrix constrIneq0, Matrix jacIneq0, Matrix constrEq0, Matrix jacEq0)	Construct the barrier subproblem at a starting iterate.
	BarrierSubproblem(Matrix x0, Matrix s0, ToDoubleBiFunction<Matrix,Object> fun, BiFunction<Matrix,Object,Matrix> grad, LagrangeHessian lagrHess, long nVars, long nIneq, long nEq, Constraint constr, Jacobian jac, double barrierParameter, double tolerance, boolean[] enforceFeasibility, GlobalStoppingCriteria globalStopCriteria, double xtol, double fun0, Matrix grad0, Matrix constrIneq0, Matrix jacIneq0, Matrix constrEq0, Matrix jacEq0)	Construct the barrier subproblem at a starting iterate.
	BarrierSubproblem(Matrix x0, Matrix s0, ToDoubleBiFunction<Matrix,Object> fun, BiFunction<Matrix,Object,Matrix> grad, LagrangeHessian lagrHess, long nVars, long nIneq, long nEq, Constraint constr, Jacobian jac, double barrierParameter, double tolerance, boolean[] enforceFeasibility, GlobalStoppingCriteria globalStopCriteria, double xtol, double fun0, Matrix grad0, Matrix constrIneq0, Matrix jacIneq0, Matrix constrEq0, Matrix jacEq0)	Construct the barrier subproblem at a starting iterate.
	LagrangianHessian(int n, Function<Matrix,Matrix> objectiveHess, LagrangianHessian.ConstraintHessian constraintsHess)	Build a Lagrangian Hessian for an n-variable problem.
	LagrangianHessian(int n, Function<Matrix,Matrix> objectiveHess, LagrangianHessian.ConstraintHessian constraintsHess)	Build a Lagrangian Hessian for an n-variable problem.
	NonlinearConstraint(Function<Matrix,Matrix> fun, double[] lb, double[] ub)
	NonlinearConstraint(Function<Matrix,Matrix> fun, double[] lb, double[] ub)
	NonlinearConstraint(Function<Matrix,Matrix> fun, double[] lb, double[] ub, boolean[] keepFeasible)
	NonlinearConstraint(Function<Matrix,Matrix> fun, double[] lb, double[] ub, boolean[] keepFeasible)
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, double[] lb, double[] ub)	Convenience overload with neither constraint Hessian nor keepFeasible flags.
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, double[] lb, double[] ub)	Convenience overload with neither constraint Hessian nor keepFeasible flags.
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, double[] lb, double[] ub, boolean[] keepFeasible)	Convenience overload: as NonlinearConstraint(Function, Function, BiFunction, double[], double[], boolean[]) with no constraint Hessian.
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, double[] lb, double[] ub, boolean[] keepFeasible)	Convenience overload: as NonlinearConstraint(Function, Function, BiFunction, double[], double[], boolean[]) with no constraint Hessian.
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, double[] lb, double[] ub, boolean[] keepFeasible)	Build a nonlinear constraint with explicit constraint Hessian.
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, double[] lb, double[] ub, boolean[] keepFeasible)	Build a nonlinear constraint with explicit constraint Hessian.
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, double[] lb, double[] ub, boolean[] keepFeasible)	Build a nonlinear constraint with explicit constraint Hessian.
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, double[] lb, double[] ub, boolean[] keepFeasible)	Build a nonlinear constraint with explicit constraint Hessian.
	NonlinearConstraint(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, double[] lb, double[] ub, boolean[] keepFeasible)	Build a nonlinear constraint with explicit constraint Hessian.
	NonlinearVectorFunction(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, Matrix x0, Optional<Boolean> sparseJacobian)	Build a nonlinear vector function and cache f, J at x0.
	NonlinearVectorFunction(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, Matrix x0, Optional<Boolean> sparseJacobian)	Build a nonlinear vector function and cache f, J at x0.
	NonlinearVectorFunction(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, Matrix x0, Optional<Boolean> sparseJacobian)	Build a nonlinear vector function and cache f, J at x0.
	NonlinearVectorFunction(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, Matrix x0, Optional<Boolean> sparseJacobian)	Build a nonlinear vector function and cache f, J at x0.
	NonlinearVectorFunction(Function<Matrix,Matrix> fun, Function<Matrix,Matrix> jac, BiFunction<Matrix,Matrix,Matrix> hess, Matrix x0, Optional<Boolean> sparseJacobian)	Build a nonlinear vector function and cache f, J at x0.

Uses of Matrix in de.labathome.trustconstr.interfaces

Methods in de.labathome.trustconstr.interfaces that return Matrix

Modifier and Type	Method	Description
Matrix	HessianProduct.apply(Matrix x, Matrix p, Object args)
Matrix	LinearOperator.apply(Matrix x)	Apply the linear operator to a given vector.
Matrix	Constraint.constrEq(Matrix x)
Matrix	Constraint.constrIneq(Matrix x)
Matrix	VectorFunctionLike.f()
Matrix	VectorFunctionLike.fun(Matrix x)	Evaluate the function at x, updating any internal cache.
Matrix	HessianUpdateStrategy.getMatrix()	Return current internal matrix.
Matrix	VectorFunctionLike.hess(Matrix x, Matrix v)	Evaluate the constraint Hessian-of-Lagrangian Sum_i v[i] * H_{f_i}(x) at x for the given multiplier vector v.
Matrix	VectorFunctionLike.J()
Matrix	VectorFunctionLike.jac(Matrix x)	Evaluate the Jacobian at x, updating any internal cache.
Matrix	Jacobian.jacEq(Matrix x)
Matrix	Jacobian.jacIneq(Matrix x)
Matrix	VectorFunctionLike.v()

Methods in de.labathome.trustconstr.interfaces with parameters of type Matrix

Modifier and Type	Method	Description
Matrix	HessianProduct.apply(Matrix x, Matrix p, Object args)
Matrix	LinearOperator.apply(Matrix x)	Apply the linear operator to a given vector.
Matrix	Constraint.constrEq(Matrix x)
Matrix	Constraint.constrIneq(Matrix x)
Matrix	VectorFunctionLike.fun(Matrix x)	Evaluate the function at x, updating any internal cache.
FunctionAndConstraint	IFunctionAndConstraint.funAndConstr(Matrix x)
GradientAndJacobian	IGradientAndJacobian.gradAndJac(Matrix z)
Matrix	VectorFunctionLike.hess(Matrix x, Matrix v)	Evaluate the constraint Hessian-of-Lagrangian Sum_i v[i] * H_{f_i}(x) at x for the given multiplier vector v.
Matrix	VectorFunctionLike.jac(Matrix x)	Evaluate the Jacobian at x, updating any internal cache.
Matrix	Jacobian.jacEq(Matrix x)
Matrix	Jacobian.jacIneq(Matrix x)
LinearOperator	LagrangeHessian.lagrHess(Matrix x, Matrix v)
boolean	GlobalStoppingCriteria.shouldStop(State state, Matrix x, boolean lastIterationFailed, double optimality, double constrViolation, double trustRadius, double penalty, CGInfo cgInfo, double barrierParameter, double tolerance)
boolean	StoppingCriterion.shouldStop(State state, Matrix x, boolean lastIterationFailed, double optimality, double constrViolation, double trustRadius, double penalty, CGInfo cgInfo)
void	HessianUpdateStrategy.update(Matrix deltaX, Matrix deltaG)	Update internal matrix.

Uses of Matrix in de.labathome.trustconstr.matrix

Subclasses of Matrix in de.labathome.trustconstr.matrix

Modifier and Type	Class	Description
final class	DenseMatrix	Dense matrix backed by a column-major double[] buffer of length rows * cols.
class	SparseMatrix	Sparse matrix backed by a Dictionary-of-Keys (DOK) representation: a HashMap keyed by the encoded (row, col) index.

Methods in de.labathome.trustconstr.matrix that return Matrix

Modifier and Type	Method	Description
abstract Matrix	Matrix.abs()
Matrix	SparseMatrix.abs()
Matrix	Matrix.absInPlace()	In-place absolute value.
abstract Matrix	Matrix.copy()
Matrix	SparseMatrix.copy()
static Matrix	Matrix.Factory.copyFromMatrix(Matrix m)
static Matrix	MatrixOps.diag(double[] diagonal)	Build a sparse diagonal matrix with the given entries on the diagonal.
abstract Matrix	Matrix.divide(double alpha)
abstract Matrix	Matrix.divide(Matrix elementwise)
Matrix	SparseMatrix.divide(double alpha)
Matrix	SparseMatrix.divide(Matrix elementwise)
Matrix	Matrix.fill(double v)	In-place fill with the given scalar.
protected Matrix	Matrix.mapAdd(double scalar)	Helper used by plus(double) and minus(double): returns a fresh dense matrix with scalar added to every entry.
Matrix	Matrix.minus(double scalar)
abstract Matrix	Matrix.minus(Matrix other)
Matrix	SparseMatrix.minus(Matrix other)
abstract Matrix	Matrix.mtimes(Matrix other)
Matrix	SparseMatrix.mtimes(Matrix other)
Matrix	Matrix.plus(double scalar)
abstract Matrix	Matrix.plus(Matrix other)
Matrix	SparseMatrix.plus(Matrix other)
Matrix	Matrix.selectColumns(long col)
Matrix	Matrix.solve(Matrix rhs)	Solve this * x = rhs via LAPACK dgesv (general LU).
static Matrix	MatrixOps.sparse(Matrix A)	Materialise the non-zero entries of a possibly-dense matrix into a SparseMatrix (DOK-backed).
Matrix	Matrix.subMatrix(long r0, long c0, long r1, long c1)	Slice rows [r0..r1] and columns [c0..c1] (inclusive).
abstract Matrix	Matrix.times(double alpha)
abstract Matrix	Matrix.times(Matrix elementwise)
Matrix	SparseMatrix.times(double alpha)
Matrix	SparseMatrix.times(Matrix elementwise)
abstract Matrix	Matrix.transpose()
Matrix	SparseMatrix.transpose()

Methods in de.labathome.trustconstr.matrix with parameters of type Matrix

Modifier and Type	Method	Description
static DenseMatrix	DenseMatrix.copyFromMatrix(Matrix m)	Densify any Matrix: if m is already a DenseMatrix, return a DenseMatrix.copy(); otherwise materialise via DenseMatrix.getAsDouble(long, long).
static DenseMatrix	DenseMatrix.Factory.copyFromMatrix(Matrix m)
static Matrix	Matrix.Factory.copyFromMatrix(Matrix m)
static SparseMatrix	SparseMatrix.Factory.copyFromMatrix(Matrix m)
DenseMatrix	DenseMatrix.divide(Matrix elementwise)
abstract Matrix	Matrix.divide(Matrix elementwise)
Matrix	SparseMatrix.divide(Matrix elementwise)
boolean	Matrix.equalsContent(Matrix other)
DenseMatrix	DenseMatrix.minus(Matrix other)
abstract Matrix	Matrix.minus(Matrix other)
Matrix	SparseMatrix.minus(Matrix other)
DenseMatrix	DenseMatrix.mtimes(Matrix other)
abstract Matrix	Matrix.mtimes(Matrix other)
Matrix	SparseMatrix.mtimes(Matrix other)
DenseMatrix	DenseMatrix.plus(Matrix other)
abstract Matrix	Matrix.plus(Matrix other)
Matrix	SparseMatrix.plus(Matrix other)
Matrix	Matrix.solve(Matrix rhs)	Solve this * x = rhs via LAPACK dgesv (general LU).
static Matrix	MatrixOps.sparse(Matrix A)	Materialise the non-zero entries of a possibly-dense matrix into a SparseMatrix (DOK-backed).
DenseMatrix	DenseMatrix.times(Matrix elementwise)
abstract Matrix	Matrix.times(Matrix elementwise)
Matrix	SparseMatrix.times(Matrix elementwise)
static DenseMatrix	Matrix.Factory.vertCat(Matrix... parts)	Vertical concatenation: stack the input matrices on top of each other.

Uses of Matrix in de.labathome.trustconstr.records

Fields in de.labathome.trustconstr.records declared as Matrix

Modifier and Type	Field	Description
Matrix[]	OptimizeResult.constr	List of constraint values at the solution.
Matrix[]	State.constr	Constraint residual vectors, one entry per constraint source.
Matrix	OptimizeResult.grad	[n] Gradient of the objective function at the solution.
Matrix	State.grad	Gradient gradf(x) at the current iterate.
Matrix[]	OptimizeResult.jac	List of the Jacobian matrices of the constraints at the solution.
Matrix[]	State.jac	Constraint Jacobians, one entry per constraint source.
Matrix	OptimizeResult.lagrangianGrad	[n] Gradient of the Lagrangian function at the solution.
Matrix	State.lagrangianGrad	Lagrangian gradient gradf(x) + Sum v_i gradc_i(x) at the current iterate.
Matrix[]	OptimizeResult.v	List of the Lagrange multipliers for the constraints at the solution.
Matrix[]	State.v	Lagrange multipliers, one entry per constraint source.
Matrix	CGInfo.x	Solution vector of the EQP problem (n x 1).
Matrix	OptimizeResult.x	[n] Solution found.
Matrix	State.x	Current iterate (n x 1).

Fields in de.labathome.trustconstr.records with type parameters of type Matrix

Modifier and Type	Field	Description
List<Matrix>	CGInfo.allVecs	All intermediate iterates, when returnAll=true; otherwise null.

Methods in de.labathome.trustconstr.records that return Matrix

Modifier and Type	Method	Description
Matrix	Sparsity.A()
Matrix	FiniteDifferenceOptions.absStep()
Matrix	FunctionAndConstraint.c()
Matrix	EqIneqSplit.eq()	Returns the value of the eq record component.
Matrix	GradientAndJacobian.grad()
Matrix	AdjustedDifferencingScheme.hAdjusted()
Matrix	EqIneqSplit.ineq()	Returns the value of the ineq record component.
Matrix	GradientAndJacobian.jac()
Matrix	ApproxDerivativeResult.jacobian()	Returns the value of the jacobian record component.
Matrix	Bounds.lb()
Matrix	FiniteDifferenceBounds.lb()
Matrix	FiniteDifferenceOptions.relStep()
Matrix	Bounds.ub()
Matrix	FiniteDifferenceBounds.ub()
Matrix	StatefulResult.v()	Final Lagrange multiplier vector from the inner SQP loop.
Matrix	StatefulResult.x()
Matrix	PreparedConstraint.x0()

Methods in de.labathome.trustconstr.records with parameters of type Matrix

Modifier and Type	Method	Description
FiniteDifferenceOptions.FiniteDifferenceOptionsFactory	FiniteDifferenceOptions.FiniteDifferenceOptionsFactory.absStep(Matrix absStep)
static Sparsity	Sparsity.of(Matrix A)	Convenience factory: derive the column grouping from A via NumDiff.groupColumns(Matrix).
static Sparsity	Sparsity.of(Matrix A, int[] order)	Convenience factory with a user-supplied column ordering.
FiniteDifferenceOptions.FiniteDifferenceOptionsFactory	FiniteDifferenceOptions.FiniteDifferenceOptionsFactory.relStep(Matrix relStep)
Residual	Bounds.residual(Matrix x)	Lower and upper residuals at x: sl = x - lb (positive iff lower bound satisfied), sb = ub - x (positive iff upper bound satisfied).
double[]	PreparedConstraint.violation(Matrix x)	Compute the per-row constraint violation at x.

Constructors in de.labathome.trustconstr.records with parameters of type Matrix

Modifier	Constructor	Description
	AdjustedDifferencingScheme(Matrix hAdjusted, boolean[] useOneSided)
	ApproxDerivativeResult(Matrix jacobian, Map<String,Object> info)	Creates an instance of a ApproxDerivativeResult record class.
	Bounds(Matrix lb, Matrix ub, boolean keepFeasible)
	EqIneqSplit(Matrix eq, Matrix ineq)	Creates an instance of a EqIneqSplit record class.
	FiniteDifferenceBounds(Matrix lb, Matrix ub, boolean keepFeasible)
	FunctionAndConstraint(double f, Matrix c)
	GradientAndJacobian(Matrix grad, Matrix jac)
	PreparedConstraint(LinearConstraint lc, Matrix x0, Optional<Boolean> sparseJacobian)	Prepare a LinearConstraint.
	PreparedConstraint(NonlinearConstraint nc, Matrix x0, Optional<Boolean> sparseJacobian, FiniteDifferenceBounds finiteDiffBounds)	Prepare a NonlinearConstraint.
	PreparedConstraint(Bounds bounds, Matrix x0, Optional<Boolean> sparseJacobian)	Prepare Bounds (folding them into the constraint set as the trivial linear constraint lb &le; x &le; ub with A = I).
	Sparsity(Matrix A, int[] sparsityGroups)
	StatefulResult(Matrix x, State state)
	StatefulResult(Matrix x, State state, Matrix v)

Uses of Matrix in de.labathome.trustconstr.sparse

Methods in de.labathome.trustconstr.sparse with parameters of type Matrix

Modifier and Type	Method	Description
static CSRMatrix	CSRMatrix.fromMatrix(Matrix m)	Build from any Matrix.