# A Non-Monotone Conjugate Subgradient Type Method for Minimization of Convex Functions

## TL;DR

This paper introduces a conjugate subgradient method for convex non-differentiable function minimization that avoids line-search and monotonicity constraints, reducing implementation complexity while maintaining efficiency.

## Contribution

It presents a novel non-monotone conjugate subgradient method that eliminates line-search and adapts step-sizes based on iteration behavior, improving practicality.

## Key findings

- Preliminary computational results confirm the method's efficiency.
- The approach reduces implementation complexity compared to traditional methods.
- It does not require monotone decrease of the goal function.

## Abstract

We suggest a conjugate subgradient type method without any line-search for minimization of convex non differentiable functions. Unlike the custom methods of this class, it does not require monotone decrease of the goal function and reduces the implementation cost of each iteration essentially. At the same time, its step-size procedure takes into account behavior of the method along the iteration points. Preliminary results of computational experiments confirm efficiency of the proposed modification.