Relevant bibliographies by topics / Optimization theory and applications

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  1. Journal articles

Academic literature on the topic 'Optimization theory and applications'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Optimization theory and applications"

1

Wang, Bin, Lotfollah Najjar, Neal N. Xiong, and Rung Ching Chen. "Stochastic Optimization: Theory and Applications." Journal of Applied Mathematics 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/949131.

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2

Yinyu, Ye, Jiang Bo, Li Xiaocheng, et al. "Modern optimization theory and applications." SCIENTIA SINICA Mathematica 50, no. 7 (2020): 899. http://dx.doi.org/10.1360/ssm-2020-0035.

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3

Consigli, Giorgio, Darinka Dentcheva, and Francesca Maggioni. "Stochastic optimization: theory and applications." Annals of Operations Research 292, no. 2 (2020): 575–80. http://dx.doi.org/10.1007/s10479-020-03672-y.

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4

Werner, Frank. "Discrete Optimization: Theory, Algorithms, and Applications." Mathematics 7, no. 5 (2019): 397. http://dx.doi.org/10.3390/math7050397.

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5

Bertsimas, Dimitris, David B. Brown, and Constantine Caramanis. "Theory and Applications of Robust Optimization." SIAM Review 53, no. 3 (2011): 464–501. http://dx.doi.org/10.1137/080734510.

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6

Chen, Bo, Peter Gritzmann, and Silvano Martello. "Combinatorial optimization: theory, computation, and applications." Discrete Applied Mathematics 217 (January 2017): 1–2. http://dx.doi.org/10.1016/j.dam.2016.10.011.

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7

Larsson, Torbjörn, Michael Patriksson, and Ann-Brith Strömberg. "Conditional subgradient optimization — Theory and applications." European Journal of Operational Research 88, no. 2 (1996): 382–403. http://dx.doi.org/10.1016/0377-2217(94)00200-2.

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8

Gauci, John Baptist, and Silvano Martello. "Theory and applications in combinatorial optimization." Journal of Combinatorial Optimization 42, no. 2 (2021): 207–11. http://dx.doi.org/10.1007/s10878-021-00785-3.

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9

El-Shorbagy, M. A., and Aboul Ella Hassanien. "Particle Swarm Optimization from Theory to Applications." International Journal of Rough Sets and Data Analysis 5, no. 2 (2018): 1–24. http://dx.doi.org/10.4018/ijrsda.2018040101.

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Abstract:
Particle swarm optimization (PSO) is considered one of the most important methods in swarm intelligence. PSO is related to the study of swarms; where it is a simulation of bird flocks. It can be used to solve a wide variety of optimization problems such as unconstrained optimization problems, constrained optimization problems, nonlinear programming, multi-objective optimization, stochastic programming and combinatorial optimization problems. PSO has been presented in the literature and applied successfully in real life applications. In this paper, a comprehensive review of PSO as a well-known
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10

Tsai, Jung-Fa, John Gunnar Carlsson, Dongdong Ge, Yi-Chung Hu, and Jianming Shi. "Optimization Theory, Methods, and Applications in Engineering." Mathematical Problems in Engineering 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/759548.

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