article · 2013

Semidefinite Programming

Cristóbal Guzmán, Lovász Theta Function

Cited by 0Abstract and details only

Abstract

• SDP gives best relaxations known for some combinatorial optimization problems. • Medium/large scale SDP are beyond the grasp of computational tractability (although polynomial). • Many combinatorial applications are invariant under a group action. • These symmetries lead to nice block decompositions of the SDP, making them computationally tractable. 3 / 74 Objectives • Show the modeling capabilities of SDP. • Show through examples how to exploit symmetries for simplifying SDP formulations. • Generalize SDP models to infinite dimensions, through combinatorial applications. 4 / 74

Cite this paper

APA 7

Guzmán, C., & Function, L. T. (2013). Semidefinite Programming.

MLA 9

Guzmán, Cristóbal, and Lovász Theta Function. “Semidefinite Programming.” 2013.

Chicago

Guzmán, Cristóbal, and Lovász Theta Function. 2013. “Semidefinite Programming.”

Harvard

Guzmán, C. and Function, L.T. (2013) ‘Semidefinite Programming’.

BibTeX

@article{guzman2013semidefinite,
  title = {{Semidefinite Programming}},
  author = {Guzmán, Cristóbal and Function, Lovász Theta},
  year = {2013}
}

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