Modern data analysis requires solving hard optimization problems with a large number of parameters and a large number of constraints. A successful approach is to replace these hard problems by ...

Linear Programs (LPs) and Semidefinite Programs (SDPs) are central tools in the design and analysis of algorithms. In this course, we will study the mathematical foundations behind these convex ...

Inference problems on graphs arise naturally when trying to make sense of network data. Oftentimes, these problems are formulated as intractable optimization programs. This renders the need for fast ...

1 Computer Science Department, University Dr Moulay Tahar of Saida, Saida, Algeria. 2 Computer Science Department, University Djillali Liabes of SidiBel Abbes, SidiBel Abbes, Algeria. Before going ...

This paper provides a short introduction to optimization problems with semidefinite constraints. Basic duality and optimality conditions are presented. For linear semidefinite programming some ...

We give the first approximation algorithm for mixed packing and covering semidefinite programs (SDPs) with polylogarithmic dependence on width. Mixed packing and covering SDPs constitute a fundamental ...

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Polynomial optimization problem solver. Uses relaxation to convert the problem into Semidefinite programming. Can be also used just as Semidefinite programming solver.

Abstract: We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the ...

Abstract: We show that the maximum fidelity obtained by a positive partial transpose (p.p.t.) distillation protocol is given by the solution to a certain semidefinite program. This gives a number of ...

COSMO: Accelerated ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Automatic chordal decomposition of sparse semidefinite programs.