The goal of this toolbox is to provide a fmincon function in Scilab.
The fmincon function is a linearly and nonlinearily constrained optimization
Currently, we use ipopt for the actual solver of fmincon but other solvers could
be added in the future.
We provide the optimoptions function (which superseeds the former optimset),
which manage options which are required by fmincon.
The current implementation is able to manage the following use cases. By default
we use a L-BFGS formula in order to compute an approximate of the Hessian of the
(0) The initial guess is provided in the x0 input argument.
(1) The nonlinear objective function and
the nonlinear constraints are provided.
The fun and nonlcon function can be customized to configure the nonlinear
objective function and nonlinear constraints.
In this case, we use order two finite differences with optimal step size in
order to compute the gradient of the objective function and the gradient of the
(2) The parameters are subject to bounds.
The lb and ub parameters can be configure to set
bounds on the parameters.
(3) Linear equalities and linear inequalities are managed
(4) The objective function and constraints function can
provide the exact gradients as additionnal output arguments
of their function definition.
The two "SpecifyObjectiveGradient" and
"SpecifyConstraintGradient" options can be turned
on for that purpose.
(5) Efficient approximation of the sparse Hessian by finite differences taking
into account the sparsity pattern is now possible (1.0.2 feature)
(6) Hessian can now be computed in objective function
(HessianFcn="objective" option) (new 1.0.3 feature)
* fmincon : Solves a linearly and/or nonlinearily constrained optimization
* optimoptions : Configures and returns an updated optimization data
Copyright (C) 2010 - DIGITEO - Michael Baudin
Copyright (C) 2020-2021 - Stephane Mottelet
This toolbox is released under the GPL licence :