NonSmooth Optimization (NSO) Software

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NSO Software available here

LMBM	Limited memory bundle method for large-scale nonsmooth, possibly nonconvex optimization by N. Karmitsa (Fortran 77 and mex-driver for MatLab users).
LDGB	Limited memory Discrete Gradient Bundle solver for derivative free general, possible nonconvex, nonsmooth minimization by N. Karmitsa (Fortran 95). To apply LDGB, one only needs to compute at every point the value of the objective function. The subgradient will be approximated. You can also use this code as Fortran 95 version of LMBM (due to some implementational facts it might use less subgradient evaluations than the previous version).
MPBNGC	Proximal bundle method for nonsmooth possibly nonconvex minimization by M.M. Mäkelä (Fortran 77). The code includes the constraint handling (bound constraints, linear constraints, and nonlinear/nonsmooth constraints). MPBNGC can also be used (free for academic purposes) via WWW-NIMBUS -system.
DGM	Discrete gradient solver for derivative free optimization by A. Bagirov, B. Karasozen and M. Sezer (Fortran 77). To apply DGM, one only needs to compute at every point the value of the objective function. The subgradient will be approximated.
QSM	Quasi-secant solver for nonsmooth possibly nonconvex minimization by A. Bagirov and A. Ganjehlou (Fortran 77). The user can employ either analytically calculated or approximated subgradients in his experiments (this can be done automatically by selecting one parameter).

Limited memory bundle method for large-scale nonsmooth, possibly nonconvex optimization by N. Karmitsa (Fortran 77 and mex-driver for MatLab users).

LDGB

Limited memory Discrete Gradient Bundle solver for derivative free general, possible nonconvex, nonsmooth minimization by N. Karmitsa (Fortran 95). To apply LDGB, one only needs to compute at every point the value of the objective function. The subgradient will be approximated. You can also use this code as Fortran 95 version of LMBM (due to some implementational facts it might use less subgradient evaluations than the previous version).

MPBNGC

Proximal bundle method for nonsmooth possibly nonconvex minimization by M.M. Mäkelä (Fortran 77). The code includes the constraint handling (bound constraints, linear constraints, and nonlinear/nonsmooth constraints). MPBNGC can also be used (free for academic purposes) via WWW-NIMBUS -system.

DGM

Discrete gradient solver for derivative free optimization by A. Bagirov, B. Karasozen and M. Sezer (Fortran 77). To apply DGM, one only needs to compute at every point the value of the objective function. The subgradient will be approximated.

QSM

Quasi-secant solver for nonsmooth possibly nonconvex minimization by A. Bagirov and A. Ganjehlou (Fortran 77). The user can employ either analytically calculated or approximated subgradients in his experiments (this can be done automatically by selecting one parameter).

Links to some other NSO solvers and softwares

SolvOpt Solver for local nonlinear optimization problems is an implementation of Shor's r-algorithm by A. Kuntsevich and F. Kappel. The constraints may be taken into account by the method of exact penalization (MatLab, C and Fortran).
GANSO Programming library for Global And Non-Smooth Optimization by CIAO (C/C++).
GradSamp Gradient sampling solver by J. Burke, A. Lewis, and M. Overton (MatLab).
HANSO Hybrid Algorithm for Non-Smooth Optimization by J. Burke, A. Lewis, and M. Overton (MatLab).
OBOE Oracle Based Optimization Engine for convex minimization by J.-P. Vial and N. Sawhney (C++).
PNEW Bundle-Newton method for unconstrained and linearly constrained NSO by L. Luksan and J. Vlcek (Fortran 77).
PVAR Variable metric bundle method for unconstrained and linearly constrained NSO by L. Luksan and J. Vlcek (Fortran 77).
PBUN Proximal bundle method for unconstrained and linearly constrained NSO by L. Luksan and J. Vlcek (Fortran 77).
PMIN solver for MinMax-problems by L. Luksan (Fortran 77).

Nonsmooth Test Problems

Test problems for large-scale unconstrained, bound constrained and generally constrained NSO by N.Karmitsa (Fortran77).
Test problems for small-scale unconstrained and linearly constrained NSO by L. Luksan (Fortran77).

Solver-o-matic

Solver-o-matic is an online decision tree for choosing a NSO solver. Solver-o-matic will tell you which method/solver is the most suitable for solving your problem.