Logspace Sequential Quadratic Programming for Design Optimization

TL;DR

This paper introduces Logspace Sequential Quadratic Programming (LSQP), a new optimization algorithm that leverages log-convex structures to improve efficiency in design problems, reducing iterations by 40-70%.

Contribution

The paper develops LSQP, a novel modification of SQP that exploits log-convexity for faster convergence without altering black box analysis methods.

Findings

LSQP achieves 40-70% fewer iterations than traditional SQP.

The method maintains compatibility with existing black box analysis tools.

Significant computational savings are demonstrated in design optimization problems.

Abstract

A novel approach to exploiting the log-convex structure present in many design problems is developed by modifying the classical Sequential Quadratic Programming (SQP) algorithm. The modified algorithm, Logspace Sequential Quadratic Programming (LSQP), inherits some of the computational efficiency exhibited by log-convex methods such as Geometric Programing and Signomial Programing, but retains the the natural integration of black box analysis methods from SQP. As a result, significant computational savings is achieved without the need to invasively modify existing black box analysis methods prevalent in practical design problems. In the cases considered here, the LSQP algorithm shows a 40-70% reduction in number of iterations compared to SQP.