Semi–definite programming for the nearest circulant semi–definite matrix problem

A Simple Path-following Algorithm for the Feasibility Problem in Semidefinite Programming and for Matrix Scaling over the Semidefinite Cone

Let E be the Hilbert space of symmetric matrices of the form diag(A,M), where A is n× n, and M is an l× l diagonal matrix, and the inner product 〈x, y〉 ≡ Trace(xy). Given x ∈ E, we write x ≥ 0 (x > 0) if it is positive semidefinite (positive definite). Let Q : E → E be a symmetric positive semidefinite linear operator, and μ = min{φ(x) = 0.5Trace(xQx) : ‖x‖ = 1, x ≥ 0}. The feasibility problem ...

Kernel Matrix Completion by Semidefinite Programming

Variational density matrix optimization using semidefinite programming

programming Brecht Verstichel, ∗ Helen van Aggelen, Dimitri Van Neck, Paul W. Ayers, and Patrick Bultinck Center for Molecular Modeling, Ghent University, Technologiepark 903, 9052 Zwijnaarde, Belgium Department of Inorganic and Physical Chemistry, Ghent University, Krijgslaan 281 (S3), 9000 Gent, Belgium Department of Chemistry, McMaster University, Hamilton, Ontario, L8S 4M1, Canada Abstract ...

A Recurrent Neural Network Model for Solving Linear Semidefinite Programming

In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...

Semidefinite Programming

3 Why Use SDP? 5 3.1 Tractable Relaxations of Max-Cut . . . . . . . . . . . . . . . . . . . . . . . . 5 3.1.1 Simple Relaxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.2 Trust Region Relaxation . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.3 Box Constraint Relaxation . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.4 Eigenvalue Bound . . . . . . . . . . . . ...