Second-order weakly composed adjacent-generalized contingent epiderivatives and applications to composite set-valued optimization problems

2021 ◽  
Author(s):  
Tian Tang ◽  
Qilin Wang ◽  
Xiaoyan Zhang ◽  
Yuwen Zhai
Keyword(s):  
Optimization Problems ◽  
Second Order

scholarly journals Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

2021 ◽  
Vol 11 (8) ◽  
pp. 3430
Author(s):  
Erik Cuevas ◽  
Héctor Becerra ◽  
Héctor Escobar ◽  
Alberto Luque-Chang ◽  
Marco Pérez ◽  
...  
Keyword(s):  
Convergence Rates ◽  
Optimization Problems ◽  
Search Space ◽  
Second Order ◽  
Order System ◽  
Search Patterns ◽  
Multiple Behaviors ◽  
Complex Optimization ◽  
Second Order Systems ◽  
Order Algorithm

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.

scholarly journals An algorithm for LC1 optimization

2005 ◽  
Vol 15 (2) ◽  
pp. 301-306 ◽  
Author(s):  
Nada Djuranovic-Milicic
Keyword(s):  
Rate Of Convergence ◽  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Directional Derivative ◽  
Second Order ◽  
General Algorithm ◽  
Unconstrained Optimization Problems ◽  
Convergence Proof

In this paper an algorithm for LC1 unconstrained optimization problems, which uses the second order Dini upper directional derivative is considered. The purpose of the paper is to establish general algorithm hypotheses under which convergence occurs to optimal points. A convergence proof is given, as well as an estimate of the rate of convergence.

Mixed-integer second-order cone optimization for composite discrete ply-angle and thickness topology optimization problems

2020 ◽  
Author(s):  
Sicheng He ◽  
Mohammad Shahabsafa ◽  
Weiming Lei ◽  
Ali Mohammad-Nezhad ◽  
Tamás Terlaky ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Optimization Problems ◽  
Second Order ◽  
Mixed Integer ◽  
Second Order Cone

scholarly journals Sparse Constrained Reconstruction for Accelerating Parallel Imaging Based on Variable Splitting Method

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Wenlong Xu ◽  
Xiaofang Liu ◽  
Xia Li
Keyword(s):  
Magnetic Resonance ◽  
Parallel Imaging ◽  
Optimization Problems ◽  
Splitting Method ◽  
Second Order ◽  
Constrained Reconstruction ◽  
Reconstruction Process ◽  
First Order ◽  
Variable Splitting ◽  
Reconstruction Methods

Parallel imaging is a rapid magnetic resonance imaging technique. For the ill-conditioned problem, noise and aliasing artifacts are amplified during the reconstruction process and are serious especially for high accelerating imaging. In this paper, a sparse constrained reconstruction problem is proposed for parallel imaging, and an effective solution based on the variable splitting method is contrived. First-order and second-order norm optimization problems are first split, and then they are transferred to unconstrained minimization problem by the augmented Lagrangian method. At last, first-order norm and second-order norm optimization problems are alternatively resolved by different methods. With a discrepancy principle as the stopping criterion, analysis of simulated and actual parallel magnetic resonance image reconstruction is presented and discussed. Compared with the routine parallel imaging reconstruction methods, the results show that the noise and aliasing artifacts in the reconstructed image are evidently reduced at large acceleration factors.

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