A D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
Solving a Class of Linearly Constrained Indefinite QuadraticProblems by D.C. Algorithms
Journal of Global Optimization
Large-Scale Molecular Optimization from Distance Matrices by a D. C. Optimization Approach
SIAM Journal on Optimization
Discrete tomography by convex-concave regularization and D.C. programming
Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
Binary tomography by iterating linear programs from noisy projections
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
A branch and reduce approach for solving a class of low rank d.c. programs
Journal of Computational and Applied Mathematics
Recovering sparse signals with a certain family of nonconvex penalties and DC programming
IEEE Transactions on Signal Processing
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Exact penalty and error bounds in DC programming
Journal of Global Optimization
Sparse high-dimensional fractional-norm support vector machine via DC programming
Computational Statistics & Data Analysis
Hi-index | 0.05 |
The paper addresses an important but difficult class of concave cost supply management problems which consist in minimizing a separable increasing concave objective function subject to linear and disjunctive constraints. We first recast these problems into mixed zero-one nondifferentiable concave minimization over linear constraints problems and then apply exact penalty techniques to state equivalent nondifferentiable polyhedral DC (Difference of Convex functions) programs. A new deterministic approach based on DC programming and DCA (DC Algorithms) is investigated to solve the latter ones. Finally numerical simulations are reported which show the efficiency, the robustness and the globality of our approach.