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Conic Linear Programming

In: Linear and Nonlinear Programming

Author

Listed:
  • David G. Luenberger

    (Stanford University)

  • Yinyu Ye

    (Stanford University)

Abstract

Conic Linear Programming, hereafter CLP, is a natural extension of Linear programming (LP). In LP, the variables form a vector which is required to be component-wise nonnegative, while in CLP they are points in a pointed convex cone (see Appendix B.1 ) of an Euclidean space, such as vectors as well as matrices of finite dimensions. For example, Semidefinite programming (SDP) is a kind of CLP, where the variable points are symmetric matrices constrained to be positive semidefinite.

Suggested Citation

  • David G. Luenberger & Yinyu Ye, 2021. "Conic Linear Programming," International Series in Operations Research & Management Science, in: Linear and Nonlinear Programming, edition 5, chapter 0, pages 165-198, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-85450-8_6
    DOI: 10.1007/978-3-030-85450-8_6
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