Improving the Decomposition of Partially Separable Functions in the Context of Large-Scale Optimization: a First Approach

1994 ◽  
pp. 82-94 ◽  
Author(s):  
Andrew R. Conn ◽  
Nick Gould ◽  
Philippe L. Toint
Keyword(s):  
Large Scale ◽  
Large Scale Optimization ◽  
Separable Functions ◽  
Scale Optimization

scholarly journals Large-scale optimization of households with photovoltaic-battery system and demand response

2020 ◽  
Vol 53 (2) ◽  
pp. 12572-12577
Author(s):  
Fernando Lezama ◽  
Ricardo Faia ◽  
Omid Abrishambaf ◽  
Pedro Faria ◽  
Zita Vale
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Battery System ◽  
Scale Optimization

Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization

2021 ◽  
Vol 224 ◽  
pp. 107080
Author(s):  
Wu Deng ◽  
Shifan Shang ◽  
Xing Cai ◽  
Huimin Zhao ◽  
Yongquan Zhou ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Cooperative Coevolution ◽  
Mutation Strategy ◽  
Scale Optimization

A new three-term spectral conjugate gradient algorithm with higher numerical performance for solving large scale optimization problems based on Quasi-Newton equation

2021 ◽  
pp. 2150053
Author(s):  
Jie Guo ◽  
Zhong Wan
Keyword(s):  
Conjugate Gradient ◽  
Large Scale ◽  
Line Search ◽  
Optimization Problems ◽  
Gradient Algorithm ◽  
Large Scale Optimization ◽  
Newton Equation ◽  
Numerical Performance ◽  
Scale Optimization ◽  
Quasi Newton

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization problems. It is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line search. Global convergence is established for general objective functions if the strong Wolfe line search is used. Numerical experiments are employed to show its high numerical performance in solving large-scale optimization problems. Particularly, the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000, in comparison with some similar ones in the literature. The numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time, less number of iteration or less number of function evaluation.

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