Learn to Optimize : A Brief Overview

Ke TANG*, Xin YAO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Most optimization problems of practical significance are typically solved by highly configurable parameterized algorithms. To achieve the best performance on a problem instance, a trial-and-error configuration process is required, which is very costly and even prohibitive for problems that are already computationally intensive, e.g., optimization problems associated with machine learning tasks. In the past decades, many studies have been conducted to accelerate the tedious configuration process by learning from a set of training instances. This article refers these studies as Learn to Optimize and reviews the progress achieved.
Original languageEnglish
Article numbernwae132
JournalNational Science Review
DOIs
Publication statusE-pub ahead of print - 2 Apr 2024

Keywords

  • optimization
  • data-driven algorithm design
  • automated algorithm configuration
  • machine learning

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