Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more

TL;DR

Cyanure is an open-source software package offering advanced solvers for linear model learning, supporting various loss and regularization functions, with a user-friendly Python API and plans for multi-language support.

Contribution

It introduces a versatile, efficient toolbox for empirical risk minimization with a user-friendly API, expanding support to multiple programming languages.

Findings

Supports a wide range of loss functions and regularizations.

Provides state-of-the-art optimization algorithms.

Designed for ease of use and extensibility.

Abstract

Cyanure is an open-source C++ software package with a Python interface. The goal of Cyanure is to provide state-of-the-art solvers for learning linear models, based on stochastic variance-reduced stochastic optimization with acceleration mechanisms. Cyanure can handle a large variety of loss functions (logistic, square, squared hinge, multinomial logistic) and regularization functions (l_2, l_1, elastic-net, fused Lasso, multi-task group Lasso). It provides a simple Python API, which is very close to that of scikit-learn, which should be extended to other languages such as R or Matlab in a near future.