Solvers
A progressive decoupling algorithm for minimizing the difference of convex and weakly convex functions over a linear subspace
Matlab codes used in the manuscript are available here. The solver requires Gurobi to solve subproblems. For the Two-Stage Stochastic Standard Quadratic optimization, run the file SSQP. For the test problem Two-Stage Stochastic Programming with Decision-Dependent Probability, download the DPME solver from the authors webpage and add our two files CwCPDA.m and TwoSSP.m to DPME's folder. Run TwoSSP.
Scenario decomposition with alternating projections
Matlab codes used in the manuscript "Risk-averse stochastic programs and distributionally robust
optimization via operator splitting" are available here.
Sequential DC Programming
Matlab codes of some algorithms given in the manuscript Sequential DC Programming are available here.
Animations of some methods for nonsmooth optimization: pdf and Matlab codes
Approximate DC Decomposition Algorithm
Matlab implementation of the algorithms given in the manuscript "Nonsmooth nonconvex optimization via approximate DC decompositions". The main function is "Roda", and the full package is available here.
Proximal bundle algorithm for DC programs
Matlab implementation of the algorithms given in the manuscript "Proximal bundle methods for nonsmooth DC programming". The package with solver, 14 test problems, and the main function Main.m is available here. The package uses the Matlab function quadprog for solving the master programs. This is not the best choice: the numerical experiments in the above report were obtained by employing Gurobi to solve the master programs.
Extended Level bundle Methods
Below some supplementary results on the paper "Regularized optimization methods for convex MINLP problems".
Matlab sources and three mixed-binary stochastic problems: SLP-A, SLP-B and SLP-C are available here.
The Matlab file for generating problem instances of the considered convex MINLP with probability constraints is GenDataCCP.m. The blackbox for this problem is oracleCCP.m, which requires the Matlab implicit function mvncdf.
Supplementary results are available here.
Optimization with Copulae constraints
This is a set of Matlab routines I jointly wrote with W. van Ackooij. Numerical results are presented in our joint work "Convexity and optimization with Copulae constraints". The package contains:
Matlab source for generating problem instances of the considered chance-constrained problems
14 Archemedian copulae (as well as they gradients) were implemented
4 Solvers (being two supporting-hyperplane methods) are available
Click here to download the package.
Note: The provided oracle (black-box) solves a Conic programming problem through Gurobi. Solver 1 is supposed to work without supplementary softwares