Integrality Gaps for Strong Linear Programming and Semidefinite Programming Relaxations

Integrality Gaps for Strong Linear Programming and Semidefinite Programming Relaxations

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The inapproximability for NP-hard combinatorial optimization problems lies in the heart of theoretical computer science. A negative result can be either conditional, where the starting point is a complexity assumption, or unconditional, where the inapproximability holds for a restricted model of com...

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Bibliographic Details
Main Author: Georgiou, Konstantinos
Other Authors: Pitassi, Toniann
Language:en_ca
Published: 2010
Subjects:
integrality gap
lift and project systems
convex optimization
combinatorial optimization
linear programming relaxations
semidefinite programming relaxations
minimum vertex cover
constraint satisfaction problem
Max Cut
Lovasz-Schrijver system
Sherali-Adams system
Lasserre system
hardness of approximation
0984
0405
Online Access:http://hdl.handle.net/1807/26271

Internet

http://hdl.handle.net/1807/26271

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