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sdpt3r (version 0.3)

Semi-Definite Quadratic Linear Programming Solver

Description

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) ). This includes problems such as the nearest correlation matrix problem (Higham (2002) ), D-optimal experimental design (Smith (1918) ), Distance Weighted Discrimination (Marron and Todd (2012) ), 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) .

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Version

Install

install.packages('sdpt3r')

Monthly Downloads

35

Version

0.3

License

GPL-2 | GPL-3

Maintainer

Adam Rahman

Last Published

February 11th, 2019

Functions in sdpt3r (0.3)

Adjacency Matrix for Max-kCut

Test Vector Matrix for D-Optimal Design

f vector for the Log Chebyshev Approximation Problem

Graph Partitioning Problem

Semidefinite Quadratic Linear Programming Solver

Upper Triangular Vectorization

Symmetric Matrix for Educational Testing Problem

B Matrix for the Log Chebyshev Approximation Problem

Adjacency Matrix for Graph Partitioning Problem

Approximate Correlation Matrix for Nearest Correlation Matrix Problem

Distance Weighted Discrimination

Adjacency Matrix for Max-Cut

Configuration Matrix for Minimum Ellipse Problem

Educational Testing Problem

Lovasz Number of a Graph

Max-Cut Problem

Max-kCut Problem

The Minimum Ellipsoid Problem

An Configuration Matrix for Distance Weighted Discrimination

Ap Configuration Matrix for Distance Weighted Discrimination

D-Optimal Experimental Design

Symmetric Matrix for the Toeplitz Approximatin Problem

Nearest Correlation Matrix Problem

Create a Symmetrix Matrix

Toeplitz Approximation Problem

Adjacency Matrix on which to find the Lovasz Number

Linear Matrix Inequality 1

Linear Matrix Inequality 2

Linear Matrix Inequality 3

Log Chebyshev Approximation