A dual method for certain positive semidefinite quadratic programming problems
SIAM Journal on Scientific and Statistical Computing
Solving semidefinite quadratic problems within nonsmooth optimization algorithms
Computers and Operations Research
A bundle-Newton method for nonsmooth unconstrained minimization
Mathematical Programming: Series A and B
Globally convergent variable metric method for convex nonsmooth unconstrained minimization
Journal of Optimization Theory and Applications
Algorithm 811: NDA: algorithms for nondifferentiable optimization
ACM Transactions on Mathematical Software (TOMS)
A method of truncated codifferential with application to some problems of cluster analysis
Journal of Global Optimization
Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control
SIAM Journal on Optimization
A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
SIAM Journal on Optimization
Piecewise linear approximations in nonconvex nonsmooth optimization
Numerische Mathematik
A quasisecant method for minimizing nonsmooth functions
Optimization Methods & Software - DEDICATED TO PROFESSOR VLADIMIR F. DEMYANOV ON THE OCCASION OF HIS 70TH BIRTHDAY
Gradient set splitting in nonconvex nonsmooth numerical optimization
Optimization Methods & Software - DEDICATED TO PROFESSOR VLADIMIR F. DEMYANOV ON THE OCCASION OF HIS 70TH BIRTHDAY
Aggregate codifferential method for nonsmooth DC optimization
Journal of Computational and Applied Mathematics
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In this paper, a new algorithm to locally minimize nonsmooth functions represented as a difference of two convex functions (DC functions) is proposed. The algorithm is based on the concept of codifferential. It is assumed that DC decomposition of the objective function is known a priori. We develop an algorithm to compute descent directions using a few elements from codifferential. The convergence of the minimization algorithm is studied and its comparison with different versions of the bundle methods using results of numerical experiments is given.