NonSmooth Optimization (NSO) Software
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NSO Software available here
General Nonsmooth Optimization
LMBM  Limited memory bundle method for largescale nonsmooth, possibly nonconvex optimization by N. Karmitsa (Fortran 77 and mexdriver for MatLab users). See LDGB for Fortran 95 version of LMBM. 


Diagonal bundle solver for general, possible nonconvex, largescale nonsmooth minimization by N. Karmitsa (Fortran 95). 
MPBNGC  Proximal bundle method for nonsmooth possibly nonconvex (multiobjective) 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 WWWNIMBUS system. 
QSM  Quasisecant 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). 

Splitting metrics diagonal bundle solver for general, possible nonconvex, largescale nonsmooth minimization by N. Karmitsa (Fortran 95). 
Derivative Free Optimization
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). 


Diagonal discrete gradient bundle solver for derivative free general, possible nonconvex, nonsmooth minimization by N. Karmitsa (Fortran 95). To apply DDGBundle, one only needs to compute at every point the value of the objective function. The subgradient will be approximated. 
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. 
DC Programming
DBDC  Proximal double bundle solver for nonsmooth DC programming (difference of two convex functions) by K. Joki (Fortran 95). 

PBDC  Proximal bundle solver for nonsmooth DC programming by K. Joki (Fortran 95). 
NonsmoothDCA  Solver for nonsmooth DC programming by A. Bagirov (Fortran 77). The solver is an implemantation of wellknown DCA algorithm by Le Thi Hoai An and Pham Dinh Tao. 
TCM  Truncated codifferential solver for nonsmooth DC programming by A. Bagirov (Fortran 77). 
Links to some other NSO solvers and softwares
 OSGA MatLab package for solving largescale structured convex optimization by M. Ahookhosh.
 SolvOpt Solver for local nonlinear optimization problems is an implementation of Shor's ralgorithm 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 NonSmooth Optimization by CIAO (C/C++).
 GradSamp Gradient sampling solver by J. Burke, A. Lewis, and M. Overton (MatLab).
 HANSO Hybrid Algorithm for NonSmooth 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 BundleNewton 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 MinMaxproblems by L. Luksan (Fortran 77).
Nonsmooth Test Problems
 Test problems for largescale unconstrained, bound constrained and generally constrained NSO by N.Karmitsa (Fortran77 and Fortran95).
 Test problems for smallscale unconstrained and linearly constrained NSO by L. Luksan (Fortran77).
Solveromatic
Solveromatic is an online decision tree for choosing a NSO solver. Solveromatic will tell you which method/solver is the most suitable for solving your problem.