TMAC: A Toolbox of Modern Async-Parallel, Coordinate, Splitting, and Stochastic Methods

TL;DR

TMAC is a C++11 toolbox that implements scalable, modern asynchronous and parallel optimization algorithms suitable for large-scale, diverse problems, facilitating easy customization and multi-core utilization.

Contribution

It introduces a flexible, easy-to-modify software framework for modern optimization algorithms that supports asynchronous and parallel execution on multicore systems.

Findings

Supports both smooth and nonsmooth, convex and nonconvex problems.

Enables scalable multi-threaded execution, synchronous or asynchronous.

Facilitates easy addition of new algorithms through simple modifications.

Abstract

TMAC is a toolbox written in C++11 that implements algorithms based on a set of modern methods for large-scale optimization. It covers a variety of optimization problems, which can be both smooth and nonsmooth, convex and nonconvex, as well as constrained and unconstrained. The algorithms implemented in TMAC, such as the coordinate up- date method and operator splitting method, are scalable as they decompose a problem into simple subproblems. These algorithms can run in a multi-threaded fashion, either synchronously or asynchronously, to take advantages of all the cores available. TMAC architecture mimics how a scientist writes down an optimization algorithm. Therefore, it is easy for one to obtain a new algorithm by making simple modifications such as adding a new operator and adding a new splitting, while maintaining the multicore parallelism and other features. The package is available at https://github.com/uclaopt/TMAC.