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arXiv · 1803.04300

Neural Conditional Gradients

Patrick Schramowski·Christian Bauckhage·Kristian Kersting
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Abstract

The move from hand-designed to learned optimizers in machine learning has been quite successful for gradient-based and -free optimizers. When facing a constrained problem, however, maintaining feasibility typically requires a projection step, which might be computationally expensive and not differentiable. We show how the design of projection-free convex optimization algorithms can be cast as a learning problem based on Frank-Wolfe Networks: recurrent networks implementing the Frank-Wolfe algorithm aka. conditional gradients. This allows them to learn to exploit structure when, e.g., optimizing over rank-1 matrices. Our LSTM-learned optimizers outperform hand-designed as well learned but unconstrained ones. We demonstrate this for training support vector machines and softmax classifiers.

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Patrick Schramowski, Christian Bauckhage, Kristian Kersting. 2018-03-12. Neural Conditional Gradients. https://arxiv.org/abs/1803.04300

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