论文标题
学习凸优化模型
Learning Convex Optimization Models
论文作者
论文摘要
凸优化模型通过解决凸优化问题来预测输入中的输出。凸优化模型类别很大，并且包括特殊情况，许多众所周知的模型，例如线性和逻辑回归。我们使用最近开发的方法来区分凸优化问题的解决方案相对于其参数，我们提出了一种用于在凸优化模型中学习参数的启发式方法。我们描述了凸优化模型的三个通用类别，最大后验（MAP）模型，实用性最大化模型和代理模型，并为每个模型提供了一个数值实验。
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.
