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Topics in Semidefinite and Interior-Point Methods
 
Edited by: Panos M. Pardalos :  University of Florida, Gainesville, FL
Henry Wolkowicz :  University of Waterloo, Waterloo, ON, Canada
A co-publication of the AMS and Fields Institute
Topics in Semidefinite and Interior-Point Methods
Hardcover ISBN:  978-0-8218-0825-2
Product Code:  FIC/18
List Price: $100.00
MAA Member Price: $90.00
AMS Member Price: $80.00
eBook ISBN:  978-1-4704-2986-7
Product Code:  FIC/18.E
List Price: $94.00
MAA Member Price: $84.60
AMS Member Price: $75.20
Hardcover ISBN:  978-0-8218-0825-2
eBook: ISBN:  978-1-4704-2986-7
Product Code:  FIC/18.B
List Price: $194.00 $147.00
MAA Member Price: $174.60 $132.30
AMS Member Price: $155.20 $117.60
Add to cart
Topics in Semidefinite and Interior-Point Methods
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Topics in Semidefinite and Interior-Point Methods
Edited by: Panos M. Pardalos :  University of Florida, Gainesville, FL
Henry Wolkowicz :  University of Waterloo, Waterloo, ON, Canada
A co-publication of the AMS and Fields Institute
Hardcover ISBN:  978-0-8218-0825-2
Product Code:  FIC/18
List Price: $100.00
MAA Member Price: $90.00
AMS Member Price: $80.00
eBook ISBN:  978-1-4704-2986-7
Product Code:  FIC/18.E
List Price: $94.00
MAA Member Price: $84.60
AMS Member Price: $75.20
Hardcover ISBN:  978-0-8218-0825-2
eBook ISBN:  978-1-4704-2986-7
Product Code:  FIC/18.B
List Price: $194.00 $147.00
MAA Member Price: $174.60 $132.30
AMS Member Price: $155.20 $117.60
Add to cart
  • Book Details
     
     
    Fields Institute Communications
    Volume: 18; 1998; 250 pp
    MSC: Primary 68; 90

    This volume contains refereed papers presented at the workshop on “Semidefinite Programming and Interior-Point Approaches for Combinatorial Optimization Problems” held at The Fields Institute in May 1996. Semidefinite programming (SDP) is a generalization of linear programming (LP) in that the nonnegativity constraints on the variables are replaced by a positive semidefinite constraint on matrix variables. Many of the elegant theoretical properties and powerful solution techniques follow through from LP to SDP. In particular, the primal-dual interior-point methods, which are currently so successful for LP, can be used to efficiently solve SDP problems.

    In addition to the interesting theoretical and algorithmic questions, SDP has found many important applications in combinatorial optimization, control theory and other areas of mathematical programming. SDP is currently a very hot area of research. The papers in this volume cover a wide spectrum of recent developments in SDP. The volume would be suitable as a textbook for advanced courses in optimization.

    Titles in this series are co-published with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada).
    Readership

    Graduate students and researchers in mathematics, computer science, engineering and operations.

  • Table of Contents
     
     
    • Chapters
    • Alexander Shapiro — Optimality conditions and sensitivity analysis of cone-constrained and semi-definite programs
    • Lorant Porkolab and Leonid Khachiyan — Testing the feasibility of semidefinite programs
    • Motakuri Ramana — Polyhedra, spectrahedra, and semidefinite programming
    • Leonid Faybusovich — Infinite-dimensional semidefinite programming: Regularized determinants and self-concordant barriers
    • Monique Laurent — A tour d’horizon on positive semidefinite and Euclidean distance matrix completion problems
    • Stefan Karisch and Franz Rendl — Semidefinite programming and graph equipartition
    • Charles Johnson, Brenda Kroschel and Michael Lundquist — The totally nonnegative completion problem
    • Jun Gu — The multi-SAT algorithm
    • M Emamy-K. — How efficiently can we maximize threshold pseudo-Boolean functions?
    • Guoliang Xue, Ding-Zhu Du and Frank Hwang — Faster algorithm for shortest network under given topology
    • Audris Mockus, Jonas Mockus and Linas Mockus — Bayesian heuristic approach (BHA) and applications to discrete optimization
    • Boris Mirkin — Approximation clustering: A mine of semidefinite programming problems
    • Kurt Anstreicher and Marcia Fampa — A long-step path following algorithm for semidefinite programming problems
    • Christoph Helmberg and Robert Weismantel — Cutting plane algorithms for semidefinite relaxations
    • Etienne De Klerk, Cornelis Roos and Tamas Terlaky — Infeasible-start semidefinite programming algorithms via self-dual embeddings
    • Stefano Lucidi and Laura Palagi — Solution of the trust region problem via a smooth unconstrained reformulation
  • Requests
     
     
    Review Copy – for publishers of book reviews
    Accessibility – to request an alternate format of an AMS title
Fields Institute Communications
Volume: 18; 1998; 250 pp
MSC: Primary 68; 90

This volume contains refereed papers presented at the workshop on “Semidefinite Programming and Interior-Point Approaches for Combinatorial Optimization Problems” held at The Fields Institute in May 1996. Semidefinite programming (SDP) is a generalization of linear programming (LP) in that the nonnegativity constraints on the variables are replaced by a positive semidefinite constraint on matrix variables. Many of the elegant theoretical properties and powerful solution techniques follow through from LP to SDP. In particular, the primal-dual interior-point methods, which are currently so successful for LP, can be used to efficiently solve SDP problems.

In addition to the interesting theoretical and algorithmic questions, SDP has found many important applications in combinatorial optimization, control theory and other areas of mathematical programming. SDP is currently a very hot area of research. The papers in this volume cover a wide spectrum of recent developments in SDP. The volume would be suitable as a textbook for advanced courses in optimization.

Titles in this series are co-published with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada).
Readership

Graduate students and researchers in mathematics, computer science, engineering and operations.

  • Chapters
  • Alexander Shapiro — Optimality conditions and sensitivity analysis of cone-constrained and semi-definite programs
  • Lorant Porkolab and Leonid Khachiyan — Testing the feasibility of semidefinite programs
  • Motakuri Ramana — Polyhedra, spectrahedra, and semidefinite programming
  • Leonid Faybusovich — Infinite-dimensional semidefinite programming: Regularized determinants and self-concordant barriers
  • Monique Laurent — A tour d’horizon on positive semidefinite and Euclidean distance matrix completion problems
  • Stefan Karisch and Franz Rendl — Semidefinite programming and graph equipartition
  • Charles Johnson, Brenda Kroschel and Michael Lundquist — The totally nonnegative completion problem
  • Jun Gu — The multi-SAT algorithm
  • M Emamy-K. — How efficiently can we maximize threshold pseudo-Boolean functions?
  • Guoliang Xue, Ding-Zhu Du and Frank Hwang — Faster algorithm for shortest network under given topology
  • Audris Mockus, Jonas Mockus and Linas Mockus — Bayesian heuristic approach (BHA) and applications to discrete optimization
  • Boris Mirkin — Approximation clustering: A mine of semidefinite programming problems
  • Kurt Anstreicher and Marcia Fampa — A long-step path following algorithm for semidefinite programming problems
  • Christoph Helmberg and Robert Weismantel — Cutting plane algorithms for semidefinite relaxations
  • Etienne De Klerk, Cornelis Roos and Tamas Terlaky — Infeasible-start semidefinite programming algorithms via self-dual embeddings
  • Stefano Lucidi and Laura Palagi — Solution of the trust region problem via a smooth unconstrained reformulation
Review Copy – for publishers of book reviews
Accessibility – to request an alternate format of an AMS title
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