Paper Info
Title
Compact relaxations for polynomial programming problems
Abstract
Reduced RLT constraints are a special class of Reformulation-Linearization Technique (RLT) constraints. They apply to nonconvex (both continuous and mixed-integer) quadratic programming problems subject to systems of linear equality constraints. We present an extension to the general case of polynomial programming problems and discuss the derived convex relaxation. We then show how to perform rRLT constraint generation so as to reduce the number of inequality constraints in the relaxation, thereby making it more compact and faster to solve. We present some computational results validating our approach.
Year
DOI
Venue
2012
10.1007/978-3-642-30850-5_8
SEA
Keywords
Field
DocType
linear equality constraint,computational result,inequality constraint,reformulation-linearization technique,quadratic programming problem,reduced rlt constraint,compact relaxation,polynomial programming problem,rrlt constraint generation,general case,convex relaxation
Mathematical optimization,Constraint generation,Polynomial,Computer science,Quadratic programming,Convex relaxation,Polynomial programming
Conference
Citations 
PageRank 
References 
2
0.36
17
Authors
5
Name
Order
Citations
PageRank
Sonia Cafieri118724.47
Pierre Hansen21538135.51
Lucas Létocart311512.37
Leo Liberti41280105.20
Frédéric Messine516817.36