# A scaled conjugate gradient based direct search algorithm for high dimensional box constrained derivative free optimization

## TL;DR

This paper introduces a new high-dimensional derivative free optimization method combining scaled conjugate gradient and quadratic interpolation, showing promising results in numerical tests for box constrained problems.

## Contribution

It presents a novel direct search algorithm that leverages scaled conjugate gradient and quadratic models for efficient high-dimensional box constrained optimization.

## Key findings

- Effective in high dimensions
- Outperforms existing methods in tests
- Suitable for derivative free problems

## Abstract

In this work, we propose an efficient method for solving box constrained derivative free optimization problems involving high dimensions. The proposed method relies on exploring the feasible region using a direct search approach based on scaled conjugate gradient with quadratic interpolation models. The extensive numerical computations carried out over test problems with varying dimensions demonstrate the performance of the proposed method for derivative free optimization.