On Limited Memory Self-Scaling VM-Algorithms for Unconstrained Optimization

Abstract

Abstract In this paper, we have investigated a new scaling parameter in the standard memoryless L-BFGS algorithm. This new consideration is compared with the standard L-BFGS method under the assumption of the L-BFGS method with m=3, and by using ten nonlinear different dimensionality test problems. The new modification performs very effective numerical results compared with the standard algorithm.

Keywords

• limited memory self-scaling vm-algorithms
• unconstrained optimization
• ten nonlinear