All Classes and Interfaces
Class
Description
Base class for all convergence checker implementations.
An implementation of
LeastSquaresProblem.Evaluation that is designed for extension.Base class for implementing optimization problems.
This class implements the simplex concept.
Abstract class for Sequential Quadratic Programming solvers
Convergence Checker for ADMM QP Optimizer.
Alternative Direction Method of Multipliers Solver.
TBD.
Alternating Direction Method of Multipliers Quadratic Programming Optimizer.
Container for
ADMMQPOptimizer settings.Internal Solution for ADMM QP Optimizer.
Base class multi-start optimizer for a multivariate function.
Base class for implementing optimizers for multivariate functions.
Base class for implementing optimizers.
Powell's BOBYQA algorithm.
Constraint with lower and upper bounds: \(l \le f(x) \le u\).
Provide an interval that brackets a local optimum of a function.
For a function defined on some interval
(lo, hi), this class
finds an approximation x to the point at which the function
attains its minimum.An implementation of the active Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
for non-linear, non-convex, non-smooth, global function minimization.
Population size.
Input sigma values.
Generic constraint.
Abstract Constraint Optimizer.
This interface specifies how to check if an optimization algorithm has
converged.
Multiplexer for
ConvergenceChecker, checking all the checkers converged.Multiplexer for
ConvergenceChecker, checking one of the checkers converged.Equality Constraint.
Check if an optimization has converged based on the change in computed RMS.
Gauss-Newton least-squares solver.
Goal type for an optimization problem (minimization or maximization of
a scalar function.
Base class for implementing optimizers for multivariate scalar
differentiable functions.
Inequality Constraint with lower bound only: \(l \le f(x)\).
Starting point (first guess) of the optimization procedure.
Karush–Kuhn–Tucker Solver.
Container for Lagrange t-uple.
An adapter that delegates to another implementation of
LeastSquaresProblem.A mutable builder for
LeastSquaresProblems.This class converts
vectorial objective functions to
scalar objective functions
when the goal is to minimize them.A Factory for creating
LeastSquaresProblems.An algorithm that can be applied to a non-linear least squares problem.
The optimum found by the optimizer.
The data necessary to define a non-linear least squares problem.
An evaluation of a
LeastSquaresProblem at a particular point.This class solves a least-squares problem using the Levenberg-Marquardt
algorithm.
A set of linear inequality constraints expressed as ub>Ax>lb.
A linear constraint for a linear optimization problem.
Class that represents a set of
linear constraints.A set of linear equality constraints given as Ax = b.
Set of linear inequality constraints expressed as \( A x \gt B\).
An objective function for a linear optimization problem.
Base class for implementing linear optimizers.
Class for finding the minimum of the objective function along a given
direction.
Enumeration for localized messages formats used in exceptions messages.
Maximum number of evaluations of the function to be optimized.
Maximum number of iterations performed by an (iterative) algorithm.
This class implements the multi-directional direct search method.
Multi-start optimizer.
Special implementation of the
UnivariateOptimizer interface
adding multi-start features to an existing optimizer.Adapter for mapping bounded
MultivariateFunction to unbounded ones.Adapter extending bounded
MultivariateFunction to an unbouded
domain using a penalty function.A interface for functions that compute a vector of values and can compute their
derivatives (Jacobian).
Base class for a multivariate scalar function optimizer.
This class implements the Nelder-Mead simplex algorithm.
Non-linear conjugate gradient optimizer.
Available choices of update formulas for the updating the parameter
that is used to compute the successive conjugate search directions.
Default identity preconditioner.
A constraint for a linear optimization problem indicating whether all
variables must be restricted to non-negative values.
Scalar function to be optimized.
Gradient of the scalar function to be optimized.
Marker interface.
Common settings for all optimization problems.
Interface for validating a set of model parameters.
Pivot selection rule to the use for a Simplex solver.
This class holds a point and the value of an objective function at
that point.
This class holds a point and the vectorial value of an objective function at
that point.
Powell's algorithm.
This interface represents a preconditioner for differentiable scalar
objective function optimizers.
Quadratic programming Optimizater.
Given P, Q, d, implements \(\frac{1}{2}x^T P X + Q^T x + d\).
Types of relationships between two cells in a Solver
LinearConstraint.Search interval and (optional) start value.
Sequential Gauss-Newton least-squares solver.
Simple optimization constraints: lower and upper bounds.
Simple implementation of the
ConvergenceChecker interface using
only point coordinates.Simple implementation of the
ConvergenceChecker interface
that uses only objective function values.Simple implementation of the
ConvergenceChecker interface using
only objective function values.Simple implementation of the
ConvergenceChecker interface using
only objective function values.This class implements simplex-based direct search optimization.
Solves a linear problem using the "Two-Phase Simplex" method.
A callback object that can be provided to a linear optimizer to keep track
of the best solution found.
Sequential Quadratic Programming Optimizer.
Sequential Quadratic Programming Optimizer.
Parameter for SQP Algorithm.
A MultivariateFunction that also has a defined gradient and Hessian.
Scalar function to be optimized.
Base class for a univariate scalar function optimizer.
This class holds a point and the value of an objective function at this
point.
A interface for functions that compute a vector of values and can compute their
derivatives (Jacobian).
A MultivariateFunction that also has a defined gradient and Hessian.