Sparse Approximate Solutions to Semidefinite Programs
We propose an algorithm for approximately maximizing a concave function over the bounded semi-definite cone, which produces sparse solutions. Sparsity for SDP corresponds to low rank matrices, and is a important property for both computational as well as learning theoretic reasons. As an application, building on Aaronson’s recent work, we derive a linear time algorithm for Quantum State Tomography.
KeywordsCurvature Constant Interior Point Method Linear Time Algorithm Sparse Solution Lanczos Algorithm
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