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496
A superlinearly convergent predictorcorrector method for degenerate LCP in a wide neighborhood of the central path with O (√n L)iteration complexity
, 2006
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On Mehrotratype predictorcorrector algorithms
, 2005
"... In this paper we discuss the polynomiality of a feasible version of Mehrotra’s predictorcorrector algorithm whose variants have been widely used in several IPM based optimization packages. A numerical example is given that shows that the adaptive choice of centering parameter and correction terms i ..."
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Cited by 13 (3 self)
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In this paper we discuss the polynomiality of a feasible version of Mehrotra’s predictorcorrector algorithm whose variants have been widely used in several IPM based optimization packages. A numerical example is given that shows that the adaptive choice of centering parameter and correction terms
Local Convergence of PredictorCorrector InfeasibleInteriorPoint Algorithms for SDPs and SDLCPs
 Mathematical Programming
, 1997
"... . An example of SDPs (semidefinite programs) exhibits a substantial difficulty in proving the superlinear convergence of a direct extension of the MizunoToddYe type predictorcorrector primaldual interiorpoint method for LPs (linear programs) to SDPs, and suggests that we need to force the genera ..."
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Cited by 58 (4 self)
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the generated sequence to converge to a solution tangentially to the central path (or trajectory). A MizunoToddYe type predictorcorrector infeasibleinteriorpoint algorithm incorporating this additional restriction for monotone SDLCPs (semidefinite linear complementarity problems) enjoys superlinear
Superlinear Convergence of Infeasible PredictorCorrector PathFollowing Interior Point Algorithm for SDLCP using the HKM Direction
"... Interior point method (IPM) defines a search direction at each interior point of a region. These search directions form a direction field which in turn gives rise to a system of ordinary differential equations (ODEs). The solutions of the system of ODEs can be viewed as underlying paths in the inter ..."
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Cited by 1 (0 self)
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by an interior point algorithm behave. In this paper, we give a weak sufficient condition using these offcentral paths that guarantees superlinear convergence of a predictorcorrector pathfollowing interior point algorithm for SDLCP using the HKM direction. This sufficient condition is implied by a currently
A scaled conjugate gradient algorithm for fast supervised learning
 NEURAL NETWORKS
, 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
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Cited by 451 (0 self)
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A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural
Predictorcorrector methods for sufficient linear complementarity problems in a wide neighborhood of the central path
 Optimization Methods and Software
"... Abstract. A higher order correctorpredictor interiorpoint method is proposed for solving sufficient linear complementarity problems. The algorithm produces a sequence of iterates in the N − ∞ neighborhood of the central path. The algorithm does not depend on the handicap κ of the problem. It has O ..."
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Cited by 14 (6 self)
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O((1 + κ) √ nL) iteration complexity and is superlinearly convergent even for degenerate problems. Key words. neighborhood linear complementarity, interiorpoint, pathfollowing, correctorpredictor, wide AMS subject classifications. 90C51, 90C33 1. Introduction. The MTY predictorcorrector
Improving the Stability of PredictorCorrector Methods by Residue Smoothing
, 1988
"... Residue smoothing is usually applied in order to accelerate the convergence of iteration processes. Here, we show that residue smoothing can also be used in order to increase the stability region of predictorcorrector methods. We shall concentrate on increasing the real stability boundary. The iter ..."
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Residue smoothing is usually applied in order to accelerate the convergence of iteration processes. Here, we show that residue smoothing can also be used in order to increase the stability region of predictorcorrector methods. We shall concentrate on increasing the real stability boundary
A PredictorCorrector Method for Solving the P*matrix LCP from Infeasible Starting Points
, 1994
"... A predictorcorrector method for solving the P ()matrix linear complementarity problems from infeasible starting points is analyzed. Two matrix factorizations and two backsolves are performed at each iteration. The algorithm terminates in O \Gamma ( + 1) 2 nL \Delta steps either by finding a ..."
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Cited by 5 (4 self)
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L) iterations. The algorithm is quadratically convergent for problems having a strictly complementary solution. Key Words: linear complementarity problems, P matrices, predictorcorrector, infeasibleinterior point algorithm, polynomiality, superlinear convergence. Abbreviated Title: A predictorcorrector
Polynomial Convergence of PredictorCorrector for SDLCP Based on the MZ Family of Directions
"... We establishes the polynomial convergence of a new class of pathfollowing methods for semidefinite linear complementarity problems whose search directions belong to the class of directions introduced by Monteiro [6]. Namely, we show that the polynomial iterationcomplexity bound of the well known al ..."
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algorithms for linear programming, namely the predictorcorrector algorithm of Mizuno and Ye, carry over to the context of SDLCP.
A NonInterior PredictorCorrector PathFollowing Method for LCP
 Mathematical Programming
, 1997
"... In a previous work the authors introduced a noninterior predictorcorrector path following algorithm for the monotone linear complementarity problem. The method uses ChenHarkerKanzowSmale smoothing techniques to track the central path and employs a refined notion for the neighborhood of the ..."
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Cited by 20 (1 self)
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In a previous work the authors introduced a noninterior predictorcorrector path following algorithm for the monotone linear complementarity problem. The method uses ChenHarkerKanzowSmale smoothing techniques to track the central path and employs a refined notion for the neighborhood
Results 1  10
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496