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Conic convex programming and self-dual embedding

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  • Luo, Z-Q.
  • Sturm, J.F.
  • Zhang, S.

Abstract

How to initialize an algorithm to solve an optimization problem is of great theoretical and practical importance. In the simplex method for linear programming this issue is resolved by either the two-phase approach or using the so-called big M technique. In the interior point method, there is a more elegant way to deal with the initialization problem, viz. the self-dual embedding technique proposed by Ye, Todd and Mizuno (30). For linear programming this technique makes it possible to identify an optimal solution or conclude the problem to be infeasible/unbounded by solving its embedded self-dual problem. The embedded self-dual problem has a trivial initial solution and has the same structure as the original problem. Hence, it eliminates the need to consider the initialization problem at all. In this paper, we extend this approach to solve general conic convex programming, including semidefinite programming. Since a nonlinear conic convex programming problem may lack the so-called strict complementarity property, it causes difficulties in identifying solutions for the original problem, based on solutions for the embedded self-dual system. We provide numerous examples from semidefinite programming to illustrate various possibilities which have no analogue in the linear programming case.

Suggested Citation

  • Luo, Z-Q. & Sturm, J.F. & Zhang, S., 1998. "Conic convex programming and self-dual embedding," Econometric Institute Research Papers EI 9815, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1554
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    References listed on IDEAS

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    1. Yinyu Ye & Michael J. Todd & Shinji Mizuno, 1994. "An O(√nL)-Iteration Homogeneous and Self-Dual Linear Programming Algorithm," Mathematics of Operations Research, INFORMS, vol. 19(1), pages 53-67, February.
    2. NESTEROV , Yurii & TODD , Michael, 1995. "Primal-Dual Interior-Point Methods for Self-Scaled Cones," LIDAM Discussion Papers CORE 1995044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Luo, Z-Q. & Sturm, J.F. & Zhang, S., 1997. "Duality Results for Conic Convex Programming," Econometric Institute Research Papers EI 9719/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. NESTEROV, Yurii & TODD, Michael & YE, Yinyu, 1996. "Primal-Dual Methods and Infeasibility Detectors for Nonlinear Programming Problems," LIDAM Discussion Papers CORE 1996037, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Yao, D.D. & Zhang, S. & Zhou, X.Y., 1999. "LQ control without Ricatti equations: deterministic systems," Econometric Institute Research Papers EI 9913-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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