Package org.hipparchus.optim.nonlinear.vector.constrained
package org.hipparchus.optim.nonlinear.vector.constrained
This package provides algorithms that minimize the residuals
between observations and model values.
The
Algorithms in this category need access to a problem (represented by a
The problem can be created progressively using a
leastsquares optimizers
minimize the distance (called
cost or χ^{2}) between model and
observations.
Algorithms in this category need access to a problem (represented by a
LeastSquaresProblem
).
Such a model predicts a set of values which the algorithm tries to match
with a set of given set of observed values.
The problem can be created progressively using a
builder
or it can
be created at once using a factory
. Since:
 3.1

ClassDescriptionAbstract class for Sequential Quadratic Programming solversConvergence 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.Constraint with lower and upper bounds: \(l \le f(x) \le u\).Generic constraint.Abstract Constraint Optimizer.Equality Constraint.Inequality Constraint with lower bound only: \(l \le f(x)\).Karush–Kuhn–Tucker Solver.Container for Lagrange tuple.A set of linear inequality constraints expressed as ub>Ax>lb.A set of linear equality constraints given as Ax = b.Set of linear inequality constraints expressed as \( A x \gt B\).Quadratic programming Optimizater.Given P, Q, d, implements \(\frac{1}{2}x^T P X + Q^T x + d\).Sequential Quadratic Programming Optimizer.Sequential Quadratic Programming Optimizer.Parameter for SQP Algorithm.A MultivariateFunction that also has a defined gradient and Hessian.A MultivariateFunction that also has a defined gradient and Hessian.