Deterministic algorithms for constrained concave minimization: A unified critical survey
本文综述了约束凹最小化的确定性算法，将其分为枚举、逐次逼近和逐次划分三类，并介绍了四种基础技术，帮助研究者快速了解各类算法的优缺点。
The purpose of this article is to present a unified critical survey of the deterministic solution algorithms for constrained concave minimization. To unify and streamline the survey, the article first describes four fundamental techniques that serve as building blocks for most of these algorithms. These four techniques are extreme point ranking, concavity cut reduction, outer approximation, and branch and bound. Using these descriptions, the article then surveys the deterministic algorithms for constrained concave minimization by grouping them into three categories of approaches. These categories are enumeration, successive approximation, and successive partitioning. This strategy provides a means of efficiently conveying the essential mechanics and the relative strengths and weaknesses of most of the well-known concave minimization algorithms. © 1996 John Wiley & Sons, Inc.