Learning Convex Optimization Models
Learning Convex Optimization Models
复制标题
学习凸优化模型
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Stephen P. Boyd
中科院分区：
文献类型：
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作者：
Akshay Agrawal;Shane T. Barratt;Stephen P. Boyd
凸优化模型通过求解凸优化问题来从输入预测输出。凸优化模型的类别很大，并且包括许多众所周知的模型，如线性和逻辑回归。我们提出了一个启发式的学习参数的凸优化模型给定的输入输出对的数据集，使用最近开发的方法区分凸优化问题的解决方案，其参数。我们描述了三个一般类的凸优化模型，最大后验概率（MAP）模型，效用最大化模型和代理模型，并提出了一个数值实验。
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.