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The ellipsoid method is often mentioned in relation to the complexity of solving linear programs. Is the method however polynomial in the general non-linear convex cases? Preferably I would like a reference to a work stating the complexity of the method in such cases.

Note: I had a look at this question, but could not find what I am looking for.

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The following links refers to application of ellipsoidal algorithms with some variations to NLPs:

Paper by Drs. Rugenstein. E & Kupferschimd.M: presents results showing convergence by ellipsoidal algorithm to convex NLPs

Lecture notes from Univ of British Columbia: application of ellipsoids with separation Oracle method for nonlinear objective

Also helpful lecture notes from Georgia Tech: discussion on Dr. N.Karmakar's interior points methods.

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  • $\begingroup$ But what is the runtime complexity of the ellipsoid method in this case? $\endgroup$ Nov 26, 2023 at 18:10
  • $\begingroup$ The first paper gives an indication using cpu time. $\endgroup$ Nov 26, 2023 at 19:27
  • $\begingroup$ I read the first paper. They report cpu time in simulation experiments, but do not provide a theoretic guarantee for the run-time $\endgroup$ Nov 27, 2023 at 6:49
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The original paper by Khachiyan (1980) talks about the application to other classes of convex problems in the conclusion. The author discusses:

  1. Localization of the solutions,
  2. measure of incompatibility, and
  3. polynomial computability of functions and gradients. (like ErlingMOSEK mentions in the comments)

A more practical reference is perhaps Grötschel, Lovász, and Schrijver (1981). Here the authors seem to prove that there exists a polynomial algorithm to solve the convex problem, if and only if the separation problem can be solved in polynomial time.

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