5 papers
A projected descent method for minimization of weakly semismooth functions over polyhedral sets
Morteza Maleknia, Majid Soleimani-damaneh
This study develops a projection-based descent method for minimizing weakly semismooth functions over polyhedral sets, taking an initial step toward extending projection-based desc…
An effective subgradient algorithm via Mifflin's line search for nonsmooth nonconvex multiobjective optimization
Morteza Maleknia, Majid Soleimani-damaneh
We propose a descent subgradient algorithm for unconstrained nonsmooth nonconvex multiobjective optimization problems. To find a descent direction, we present an iterative process…
A descent subgradient method using Mifflin line search for nonsmooth nonconvex optimization
Morteza Maleknia, Majid Soleimani-damaneh
We propose a descent subgradient algorithm for minimizing a real function, assumed to be locally Lipschitz, but not necessarily smooth or convex. To find an effective descent direc…
A new method based on the bundle idea and gradient sampling technique for minimizing nonsmooth convex functions
M. Maleknia, M. Shamsi
In this paper, we combine the positive aspects of the Gradient Sampling (GS) and bundle methods, as the most efficient methods in nonsmooth optimization, to develop a robust method…
A Gradient Sampling method based on Ideal direction for solving nonsmooth nonconvex optimization problems: convergence analysis and numerical experiments
M. Maleknia, M. Shamsi
In this paper, a modification to the Gradient Sampling (GS) method for minimizing nonsmooth nonconvex functions is presented. One drawback in GS method is the need of solving a Qua…