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Sivaramakrishnan, Kartik K. - Department of Mathematics, North Carolina State University
RankTwo Relaxation Heuristics for the MaxCut problem
First Prev Next Last Go Back Full Screen Close Quit A CONIC INTERIOR POINT DECOMPOSITION APPROACH
First Prev Next Last Go Back Full Screen Close Quit A PARALLEL TWO-STAGE INTERIOR POINT
Nonlinear Programming Qualifying Exam Set by: Dr. Kartik Sivaramakrishnan
A parallel interior point decomposition algorithm for block angular semidefinite programs
Solving Semidefinite Programs via Nonlinear Programming
Towards a simplex-like method for second order cone programming
First Prev Next Last Go Back Full Screen Close Quit A CONIC INTERIOR POINT DECOMPOSITION APPROACH
The maximal stable set problem : Copositive programming and Semidefinite Relaxations
First Prev Next Last Go Back Full Screen Close Quit A PARALLEL INTERIOR POINT DECOMPOSITION
First Prev Next Last Go Back Full Screen Close Quit EXPLOITING SPARSITY AND SYMMETRY IN
First Prev Next Last Go Back Full Screen Close Quit SOLVING POLYNOMIAL OPTIMIZATION PROBLEMS USING
First Prev Next Last Go Back Full Screen Close Quit SOLVING POLYNOMIAL OPTIMIZATION PROBLEMS USING
A conic interior point decomposition approach for large scale semidefinite programming
First Prev Next Last Go Back Full Screen Close Quit A PARALLEL INTERIOR POINT DECOMPOSITION
First Prev Next Last Go Back Full Screen Close Quit A PARALLEL INTERIOR POINT DECOMPOSITION
SULLIVAN, ERIC J. Solving the Max-Cut Problem using Semidefinite Optimization in a Cutting Plane Algorithm. (Under the direction of Professor Kartik Sivaramakrishnan).
Solving Mixed Integer Linear Programs Using Branch and Cut Algorithm
CONVEX OPTIMIZATION AND INTERIOR POINT FINAL PROJECT
OPTIMIZATION TECHNIQUES FOR SOLVING BASIS PURSUIT PROBLEMS
MA 796s-OR 791k Project Report December 2007 Max-cut Problem
Communicating without errors, Lovasz theta function and
Mixed Linear And Semidefinite Programming For Combinatorial And Quadratic
s s 1 SECTIONALISING SWITCH
Linear Programming (LP) Approaches to Semidefinite Programming (SDP) Problems
Lower bounds for approximate factorizations via semidefinite programming (extended abstract)*
A Report on Approximate Graph Coloring by Semidefinite Programming
First Prev Next Last Go Back Full Screen Close Quit LAGRANGIAN RELAXATION TECHNIQUES FOR LARGE
OR 791K: Convex Optimization and Interior Point Methods December 2007 A Summary: Fastest Mixing Markov Chain on a Graph
Linear Programming (LP) Approaches to Semidefinite Programming (SDP) Problems
MA/OR/ST 706: Nonlinear Programming Class Project
Towards a simplex-like method for conic programming
Towards a practical simplex method for second order cone programming
