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Convergence Rate of Frank-Wolfe for Non-Convex Objectives

Optimization and Control 2016-07-07 v1 Machine Learning Numerical Analysis Machine Learning

Abstract

We give a simple proof that the Frank-Wolfe algorithm obtains a stationary point at a rate of O(1/t)O(1/\sqrt{t}) on non-convex objectives with a Lipschitz continuous gradient. Our analysis is affine invariant and is the first, to the best of our knowledge, giving a similar rate to what was already proven for projected gradient methods (though on slightly different measures of stationarity).

Keywords

Cite

@article{arxiv.1607.00345,
  title  = {Convergence Rate of Frank-Wolfe for Non-Convex Objectives},
  author = {Simon Lacoste-Julien},
  journal= {arXiv preprint arXiv:1607.00345},
  year   = {2016}
}

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6 pages