An Approximate Conjugate Subgradient Algorithm with Matrix Parameter for Derivative-Free Nonsmooth Optimization Problems

Abstract

We propose a derivative-free matrix conjugate-subgradient method for unconstrained nonsmooth optimization of locally Lipschitz functions. The method constructs discrete gradients using only function values and forms a finite sampled model of the Goldstein subdifferential. A minimal-norm element of the convex hull of the sampled discrete gradients is then computed and used both as a stationarity measure and as the reference vector for generating descent-oriented directions. To improve robustness beyond the basic steepest-descent direction, we introduce a matrix memory correction together with coefficient damping, diagonal scaling, bounded-angle correction, and matrix-stability safeguards. A two-point line-search procedure with enrichment is used to obtain either a serious step or an improved local model. Under suitable consistency assumptions on the discrete-gradient approximation and line-search sampling, the method generates directions satisfying a safeguarded descent property and computes approximate Goldstein stationary points. Numerical experiments on nonsmooth test problems with dimensions up to 1000 show that both proposed variants are robust for lower and medium accuracy requirements, while the matrix conjugate-subgradient variant remains the most reliable under the strictest tolerance.