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2011

Optimization of Convex Functions with Random Pursuit

Stich, Sebastian U., Müller, Christian L., Gärtner, Bernd

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We consider unconstrained randomized optimization of convex objective functions.

We analyze the Random Pursuit algorithm, which iteratively computes an approximate solution to the optimization problem by repeated optimization over a randomly chosen one-dimensional subspace.
This randomized method only uses zeroth-order information about the objective function and does not need any problem-specific parametrization.
We prove convergence and give convergence rates for smooth objectives assuming that the one-dimensional optimization can be solved exactly or approximately by an oracle.

Optimization of Convex Functions with Random Pursuit · Around

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