期刊
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
卷 8, 期 8, 页码 1355-1364出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2021.1004075
关键词
Predictive models; Convex functions; Numerical models; Logistics; Convex optimization; differentiable optimization; machine learning
Convex optimization models predict outputs by solving convex optimization problems, including well-known models like linear and logistic regression. The paper proposes a heuristic for learning parameters in convex optimization models and describes three general classes of such models, presenting numerical experiments for each.
A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cases many well-known models like linear and logistic regression. We propose a heuristic for learning the parameters in a convex optimization model given a dataset of input-output pairs, using recently developed methods for differentiating the solution of a convex optimization problem with respect to its parameters. We describe three general classes of convex optimization models, maximum a posteriori (MAP) models, utility maximization models, and agent models, and present a numerical experiment for each.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据