ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence

Published: 21 Sept 2023, Last Modified: 02 Nov 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: coordinate descent, linear convergence, primal-dual methods, empirical risk minimization, linear constraints, quantile regression
TL;DR: A novel algorithm for empirical risk minimization that significantly surpasses generic solvers and also outperforms specialized solvers including LIBLINEAR, hqreg, and Lightning.
Abstract: Empirical risk minimization (ERM) is a crucial framework that offers a general approach to handling a broad range of machine learning tasks. In this paper, we propose a novel algorithm, called ReHLine, for minimizing a set of regularized ERMs with convex piecewise linear-quadratic loss functions and optional linear constraints. The proposed algorithm can effectively handle diverse combinations of loss functions, regularization, and constraints, making it particularly well-suited for complex domain-specific problems. Examples of such problems include FairSVM, elastic net regularized quantile regression, Huber minimization, etc. In addition, ReHLine enjoys a provable linear convergence rate and exhibits a per-iteration computational complexity that scales linearly with the sample size. The algorithm is implemented with both Python and R interfaces, and its performance is benchmarked on various tasks and datasets. Our experimental results demonstrate that ReHLine significantly surpasses generic optimization solvers in terms of computational efficiency on large-scale datasets. Moreover, it also outperforms specialized solvers such as Liblinear in SVMs, hqreg in Huber minimization, and Lightning (SAGA, SAG, SDCA, SVRG) in smoothed SVMs, exhibiting exceptional flexibility and efficiency. The source code, project page, accompanying software, and the Python/R interface can be accessed through the link:
Submission Number: 11891