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Stochastic Approach to a Class of Convex Optimization Problems

Pavel Shcherbakov, Boris Polyak
We propose a new approach to solving a wide class of optimization problems which fall into the broad framework of
linear matrix inequalities and semidefinite programming. This approach based on randomization and cutting
hyperplane ideology also covers robust statements of the problem. The proposed method is easy to implement,
and it might be particularly useful in various aspects of functioning and applications of quantum computers.
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