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Semidefinite Programming Problems

  • Christodoulos A. Floudas
  • Pãnos M. Pardalos
  • Claire S. Adjiman
  • William R. Esposito
  • Zeynep H. Gümüş
  • Stephen T. Harding
  • John L. Klepeis
  • Clifford A. Meyer
  • Carl A. Schweiger
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 33)

Abstract

Semidefinite programming involves the minimization of a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Several types of problems can be transformed to this form. This constraint is in general nonlinear and nonsmooth yet convex. Semidefinite programming can be viewed as an extension of linear programming and reduces to the linear programming case when the symmetric matrices are diagonal.

Keywords

Linear Matrix Inequality Positive Semidefinite Semidefinite Programming Diagonal Covariance Matrix Semidefinite Programming Relaxation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Christodoulos A. Floudas
    • 1
  • Pãnos M. Pardalos
    • 2
  • Claire S. Adjiman
    • 1
  • William R. Esposito
    • 1
  • Zeynep H. Gümüş
    • 1
  • Stephen T. Harding
    • 1
  • John L. Klepeis
    • 1
  • Clifford A. Meyer
    • 1
  • Carl A. Schweiger
    • 1
  1. 1.Department of Chemical EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Department of Industrial and Systems EngineeringUniversity of FloridaUSA

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