scholarly journals Locating Objects in the Plane Using Global Optimization Techniques

2009 ◽  
Vol 34 (4) ◽  
pp. 837-858 ◽  
Author(s):  
Rafael Blanquero ◽  
Emilio Carrizosa ◽  
Pierre Hansen
Keyword(s):  
Global Optimization ◽  
Optimization Techniques

The study of drug–reaction relationships using global optimization techniques

2007 ◽  
Vol 22 (1) ◽  
pp. 99-126 ◽  
Author(s):  
M. A. Mammadov ◽  
A. M. Rubinov ◽  
J. Yearwood
Keyword(s):  
Global Optimization ◽  
Drug Reaction ◽  
Optimization Techniques

Tensor based approach to the numerical treatment of the parameter estimation problems in mathematical immunology

2018 ◽  
Vol 26 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Valeriya V. Zheltkova ◽  
Dmitry A. Zheltkov ◽  
Zvi Grossman ◽  
Gennady A. Bocharov ◽  
Eugene E. Tyrtyshnikov
Keyword(s):  
Global Optimization ◽  
Parameter Estimation ◽  
Optimization Techniques ◽  
Computational Method ◽  
Parameter Estimation Problem ◽  
Multiscale Models ◽  
Computational Tools ◽  
Systems Immunology ◽  
Estimation Problems ◽  
Standard Parameter

AbstractThe development of efficient computational tools for data assimilation and analysis using multi-parameter models is one of the major issues in systems immunology. The mathematical description of the immune processes across different scales calls for the development of multiscale models characterized by a high dimensionality of the state space and a large number of parameters. In this study we consider a standard parameter estimation problem for two models, formulated as ODEs systems: the model of HIV infection and BrdU-labeled cell division model. The data fitting is formulated as global optimization of variants of least squares objective function. A new computational method based on Tensor Train (TT) decomposition is applied to solve the formulated problem. The idea of proposed method is to extract the tensor structure of the optimized functional and use it for optimization. The method demonstrated a better performance in comparison with some other broadly used global optimization techniques.

scholarly journals Designing Strategic Games with Preestablished Nash Equilibrium through Artificial Inference and Global Learning

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hime A. e Oliveira Jr.
Keyword(s):  
Global Optimization ◽  
Nash Equilibrium ◽  
Solution Concept ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Strategic Games ◽  
Global Learning ◽  
Finite State ◽  
Payoff Functions ◽  
Game Designer

Abstract This work presents novel results obtained by the application of global optimization techniques to the design of finite, normal form games with mixed strategies. To that end, the Fuzzy ASA global optimization method is applied to several design examples of strategic games, demonstrating its effectiveness in obtaining payoff functions whose corresponding games present a previously established Nash equilibrium. In other words, the game designer becomes able to choose a convenient Nash equilibrium for a generic finite state strategic game and the proposed method computes payoff functions that will realize the desired equilibrium, making it possible for the players to reach the favorable conditions represented by the chosen equilibrium. Considering that game theory is a very useful approach for modeling interactions between competing agents and Nash equilibrium represents a powerful solution concept, it is natural to infer that the proposed method may be very useful for strategists in general. In summary, it is a genuine instance of artificial inference of payoff functions after a process of global machine learning, applied to their numerical components.

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