Relax, descend and certify : optimization techniques for typically tractable data problems
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In this thesis we explore different mathematical techniques for extracting information from data. In particular we focus in machine learning problems such as clustering and data cloud alignment. Both problems are intractable in the "worst case", but we show that convex relaxations can efficiently find the exact or almost exact solution for classes of "typical" instances. We study different roles that optimization techniques can play in understanding and processing data. These include efficient algorithms with mathematical guarantees, a posteriori methods for quality evaluation of solutions, and algorithmic relaxation of mathematical models. We develop probabilistic and data-driven techniques to model data and evaluate performance of algorithms for data problems.