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This paper introduces Jensen, an easily extensible and scalable toolkit forproduction-level machine learning and convex optimization. Jensen implements aframework of convex (or loss) functions, convex optimization algorithms(including Gradient Descent, L-BFGS, Stochastic Gradient Descent, ConjugateGradient, etc.), and a family of machine learning classifiers and regressors(Logistic Regression, SVMs, Least Square Regression, etc.). This frameworkmakes it possible to deploy and train models with a few lines of code, and alsoextend and build upon this by integrating new loss functions and optimizationalgorithms.

Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization