Semi-Definite Quadratic Linear Programming Solver
Control Theory
D-Optimal Experimental Design
Distance Weighted Discrimination
Educational Testing Problem
Graph Partitioning Problem
Linear Matrix Inequality 1
Linear Matrix Inequality 2
Linear Matrix Inequality 3
Log Chebyshev Approximation
Lovasz Number of a Graph
Max-Cut Problem
Max-kCut Problem
The Minimum Ellipsoid Problem
Nearest Correlation Matrix Problem
Create a Symmetrix Matrix
Semidefinite Quadratic Linear Programming Solver
Upper Triangular Vectorization
Toeplitz Approximation Problem
Solves the general Semi-Definite Linear Programming formulation using an R implementation of SDPT3 (K.C. Toh, M.J. Todd, and R.H. Tutuncu (1999) <doi:10.1080/10556789908805762>). This includes problems such as the nearest correlation matrix problem (Higham (2002) <doi:10.1093/imanum/22.3.329>), D-optimal experimental design (Smith (1918) <doi:10.2307/2331929>), Distance Weighted Discrimination (Marron and Todd (2012) <doi:10.1198/016214507000001120>), as well as graph theory problems including the maximum cut problem. Technical details surrounding SDPT3 can be found in R.H Tutuncu, K.C. Toh, and M.J. Todd (2003) <doi:10.1007/s10107-002-0347-5>.