Semidefinite Optimization

  • Wilhelm Forst
  • Dieter Hoffmann
Part of the Springer Undergraduate Texts in Mathematics and Technology book series (SUMAT)


Semidefinite optimization (SDO) differs from linear optimization (LO) in that it deals with optimization problems over the cone of symmetric positive semidefinite matrices Sn+ instead of nonnegative vectors. In many cases the objective function is linear and SDO can be interpreted as an extension of LO. It is a branch of convex optimization and contains — besides LO — linearly constrained QP, quadratically constrained QP and — for example — second-order cone programming as special cases.


Dual Problem Primal Problem Feasible Point Linear Optimization Central Path 
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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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Fak. Mathematik und Wirtschaftswissenschaften Inst. Numerische MathematikUniversität UlmUlmGermany
  2. 2.FB Mathematik und StatistikUniversität KonstanzKonstanzGermany

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