Convex Optimization: Barrier Functions And Interior-Point Methods (ResearchIndex)

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Convex Optimization: Barrier Functions And Interior-Point Methods (1998) (Make Corrections)
Levent Tunçel

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Abstract: ion of the v\Gammaspace Approach One way of achieving this symmetry (especially in primal-dual interior point algorithms) is to describe a subset G of Aut(K) such that for each (x; s) 2 int(K) \Phi int(K ), the equation D(x) = D \Gamma (s); D 2 G (7) has a unique solution. Then, in each iteration, a primal-dual algorithm can compute the unique solution above, apply the transformation D to the primal problem and D \Gamma to the dual problem and after the application of a Newton type... (Update)

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BibTeX entry: (Update)

@misc{ el-convex,
  author = "Levent Tunçel",
  title = "Convex Optimization: Barrier Functions And Interior-Point Methods",
  url = "citeseer.ist.psu.edu/224312.html" }

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Documents on the same site (http://www.is.titech.ac.jp/research/research-report/B/index.html): More
Monotonicity of Primal-Dual Interior-Point Algorithms for.. - Kojima, Tunçel (1998) (Correct)
Primal-Dual Potential Reduction Algorithms For Semidefinite.. - Tunçel (1998) (Correct)
Exploiting Sparsity in Primal-Dual Interior-Point.. - Fujisawa, Kojima, Nakata (1997) (Correct)

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