Results 1 to 10 of about 901 (96)
Polynomial Convergence of Infeasible-Interior-Point Methods over Symmetric Cones [PDF]
SIAM Journal on Optimization, 2006 We establish polynomial-time convergence of infeasible-interior-point methods for conic programs over symmetric cones using a wide neighborhood of the central path. The convergence is shown for a commutative family of search directions used in Schmieta and Alizadeh [Math. Program., 96 (2003), pp. 409-438]. Monteiro and Zhang [Math. Program., 81 (1998),
Potra, Florian A., Sheng, Rongqin
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A superquadratic infeasible-interior-point method for linear complementarity problems [PDF]
Mathematical Programming, 1996 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wright, Stephen, Zhang, Yin
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Infeasible constraint-reduced interior-point methods for linear optimization [PDF]
Optimization Methods and Software, 2012 In this paper, building on a general framework which encompasses several previously proposed approaches for dual-feasible constraint-reduced interior-point optimization, for which we prove convergence to a single point of the sequence of dual iterates, we propose a framework for ‘infeasible’ constraint-reduced interior-point optimization.
Meiyun Y. He, André L. Tits
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Infeasible Interior-Point Methods for Linear Optimization Based on Large Neighborhood [PDF]
Journal of Optimization Theory and Applications, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Asadi, A.R. (author), Roos, C. (author)
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Convergence Analysis of an Inexact Infeasible Interior Point Method for Semidefinite Programming [PDF]
Computational Optimization and Applications, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
BELLAVIA, STEFANIA, S. PIERACCINI
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Counterexample to a Conjecture on an Infeasible Interior-Point Method
SIAM Journal on Optimization, 2010 Summary: In [the second author, SIAM J. Optim. 16, No.~4, 1110--1136 (2006; Zbl 1131.90029)], Roos proved that the devised full-step infeasible algorithm has \(O(n)\) worst-case iteration complexity. This complexity bound depends linearly on a parameter \(\bar{\kappa}(\zeta)\), which is proved to be less than \(\sqrt{2n}\).
Gu, G. (author), Roos, C. (author)
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An infeasible interior point methods for convex quadratic problems
Journal of Numerical Analysis and Approximation Theory, 2018 In this paper, we deal with the study and implementation of an infeasible interior point method for convex quadratic problems (CQP). The algorithm uses a Newton step and suitable proximity measure for approximately tracing the central path and guarantees that after one feasibility step, the new iterate is feasible and suciently close to the central ...
Hayet Roumili, Nawel Boudjellal
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Convergence Analysis of the Inexact Infeasible Interior-Point Method for Linear Optimization [PDF]
Journal of Optimization Theory and Applications, 2008 This article studies the use of a primal-dual interior point method for solving large scale linear programs. The article begins with a presentation of the background to this problem and an overview of the existing literature, including the use of Preconditioned Conjugate Gradients (PCG) for inexact infeasible path-following algorithms.
Al-Jeiroudi, G., Gondzio, J.
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Improved Full-Newton Step O(nL) Infeasible Interior-Point Method for Linear Optimization [PDF]
Journal of Optimization Theory and Applications, 2009 The authors describe some improvements of the full-Newton step infeasible interior-point method (IIPM) for linear optimization introduced by C. Roos in 2006. The improved full-Newton step IIPM for linear optimization described in this paper can be seen as a homotopy method and has many interesting properties.
Gu, G. (author) +4 more
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Infeasible full Newton - step interior - point method for linear complementarity problems
Croatian Operational Research Review, 2012 In this paper we consider an Infeasible Full Newton step Interior Point- Method (IFNS -IPM) for monotone Linear Complementarity Problems (LCP). The method does not require a strictly feasible starting point. In addition, the method avoids calculation of the step size and instead takes full Newton -steps at each iteration. Iterates are kept close to the
Lešaja, Goran +2 more
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