Výsledky vyhľadávania - locally convergent nonlinear programming algorithms

A locally and cubically convergent algorithm for computing ð'µ‐eigenpairs of symmetric tensors Autor Zhao, Ruijuan, Zheng, Bing, Liang, Maolin, Xu, Yangyang

“…) for it. Our proposed MNNM method is proved to be locally and cubically convergent under some suitable conditions, which greatly improves the Newton correction method and the orthogonal Newton correction…”
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A semismooth Newton method for nonlinear symmetric cone programming Autor Kong, Lingchen, Meng, Qingmin

“…In this paper, we employ the projection operator to design a semismooth Newton algorithm for solving nonlinear symmetric cone programming (NSCP…”
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A nonlinear Lagrangian based on Fischer-Burmeister NCP function Autor Ren, Yong-Hong, Zhang, Li-Wei, Xiao, Xian-Tao

“… The convergence theorem shows that the sequence of points generated by this nonlinear Lagrange algorithm is locally convergent when the penalty parameter is less than a threshold under a set…”
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An R -linearly convergent derivative-free algorithm for nonlinear complementarity problems based on the generalized Fischer–Burmeister merit function Autor Chen, Jein-Shan, Gao, Hung-Ta, Pan, Shaohua

“…], the authors proposed a derivative-free descent algorithm for nonlinear complementarity problems (NCPs…”
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Properties of the Augmented Lagrangian in Nonlinear Semidefinite Optimization Autor Sun, J., Zhang, L. W., Wu, Y.

“… It is shown that, under a set of sufcient conditions, the augmented Lagrangian algorithm is locally convergent when the penalty parameter is larger than a certain threshold…”
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A Superlinearly Convergent Sequential Quadratically Constrained Quadratic Programming Algorithm for Degenerate Nonlinear Programming Autor Anitescu, Mihai

“… We compare this algorithm with sequential quadratic programming algorithms on several degenerate nonlinear programs…”
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An SQP method for minimization of locally Lipschitz functions with nonlinear constraints Autor Yousefpour, Rohollah, Jafari, Elham

“… This method is based on sequential quadratic programming that uses an penalty function to equilibrate among the decrease of the objective function and the feasibility of the constraints…”
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Convergence Analysis of Difference-of-Convex Algorithm with Subanalytic Data Autor Le Thi, Hoai An, Huynh, Van Ngai, Pham Dinh, Tao

“…Difference-of-Convex programming and related algorithms, which constitute the backbone of nonconvex programming and global optimization, were introduced in 1985 by Pham Dinh Tao and have been…”
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Adaptive dynamic programming Autor Murray, J.J., Cox, C.J., Lendaris, G.G., Saeks, R.

“… constraint and guarantee convergence is required. The objective of the paper is to describe an adaptive dynamic programming algorithm (ADPA…”
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Research on The Optimal Solution of Lagrangian Multiplier Function Method in Nonlinear Programming Autor Jin, Xue

“… that under appropriate conditions, the iterative point generated by the dual algorithm based on the function The column is locally convergent, and then an error estimate of the solution related to the penalty parameter is given…”
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Nonlinear robust optimization via sequential convex bilevel programming Autor Houska, Boris, Diehl, Moritz

“…In this paper, we present a novel sequential convex bilevel programming algorithm for the numerical solution of structured nonlinear min…”
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Unified convergence analysis of a second-order method of multipliers for nonlinear conic programming Autor Chen, Liang, Zhu, Junyuan, Zhao, Xinyuan

“… We first show in a unified fashion that under a few abstract assumptions, the proposed method is locally convergent and possesses a (nonasymptotic…”
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Invariant Adaptive Dynamic Programming for Discrete-Time Optimal Control Autor Zhu, Yuanheng, Zhao, Dongbin, He, Haibo

“… An invariant adaptive dynamic programming algorithm is developed to extend the method to scenarios where system dynamics is not available. Online data are utilized to learn the near-optimal policy and the invariantly admissible region. Simulated experiments verify the effectiveness of our method…”
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A Sequential Quadratic Programming Method Without A Penalty Function or a Filter for Nonlinear Equality Constrained Optimization Autor Liu, Xinwei, Yuan, Yaxiang

“…We present a sequential quadratic programming method without using a penalty function or a filter for solving nonlinear equality constrained optimization…”
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A quadratically convergent primal decomposition algorithm with soft coupling for nonlinear parameter estimation Autor Kouzoupis, Dimitris, Quirynen, Rien, Garcia, Jesus Lago, Erhard, Michael, Diehl, Moritz

“… In this paper, we propose a novel algorithm for a certain class of nonconvex, separable optimization problems that combines both distributed computations and locally quadratic convergence…”
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A trust-region LP-Newton method for constrained nonsmooth equations under Hölder metric subregularity Autor Becher, Letícia, Fernández, Damián, Ramos, Alberto

“… Our algorithm is based on a modification of the fast locally convergent Linear Programming (LP)-Newton method with a trust-region strategy for globalization that makes use of the natural merit function…”
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Nonlinear rescaling Lagrangians for nonconvex semidefinite programming Autor Zhang, Liwei, Li, Yang, Wu, Jia

“… A set of conditions on the convex real-valued functions is proposed to guarantee the convergence of nonlinear rescaling Lagrangian algorithms…”
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A truncated Newton method in an augmented Lagrangian framework for nonlinear programming Autor Di Pillo, Gianni, Liuzzi, Giampaolo, Lucidi, Stefano, Palagi, Laura

“…In this paper we propose a primal-dual algorithm for the solution of general nonlinear programming problems…”
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Inexact Interior-Point Method for PDE-Constrained Nonlinear Optimization Autor Grote, Marcus J., Huber, Johannes, Kourounis, Drosos, Schenk, Olaf

“… Because it uses the full Hessian matrix, modifying it whenever needed, the method not only is globally convergent, but also converges fast locally…”
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A superlinearly convergent ODE-type trust region algorithm for nonsmooth nonlinear equations Autor Yigui, Ou

“… Furthermore, it is proved under appropriate assumptions that this algorithm is globally and locally super-linearly convergent…”
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