Global Continuous Optimization with Error Bound and Fast Convergence

TL;DR

This paper introduces LOGO, a new global optimization algorithm that achieves fast practical convergence and provides finite-time error bounds, with applications in machine learning, engineering, and nuclear plant planning.

Contribution

The paper presents LOGO, a novel optimization algorithm that combines practical speed with theoretical guarantees, and adapts it for complex planning problems with continuous spaces.

Findings

LOGO demonstrates fast convergence on benchmark functions.

Theoretical analysis confirms finite-time error bounds.

Application to nuclear plant accident management shows practical effectiveness.

Abstract

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.