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Journal articles on the topic 'Mathematical optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Kulcsár, T., and I. Timár. "Mathematical optimization and engineering applications." Mathematical Modeling and Computing 3, no. 1 (2016): 59–78. http://dx.doi.org/10.23939/mmc2016.01.059.

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2

Bhardwaj, Suyash, Seema Kashyap, and Anju Shukla. "A Novel Approach For Optimization In Mathematical Calculations Using Vedic Mathematics Techniques." MATHEMATICAL JOURNAL OF INTERDISCIPLINARY SCIENCES 1, no. 1 (2012): 23–34. http://dx.doi.org/10.15415/mjis.2012.11002.

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3

Chawla, Dr Meenu. "Mathematical optimization techniques." Pharma Innovation 8, no. 2 (2019): 888–92. http://dx.doi.org/10.22271/tpi.2019.v8.i2n.25454.

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4

Suhl, Uwe H. "MOPS — Mathematical optimization system." European Journal of Operational Research 72, no. 2 (1994): 312–22. http://dx.doi.org/10.1016/0377-2217(94)90312-3.

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5

Requelme Ibáñez, Rosa María, Carlos Abel Reyes Alvarado, and Jorge Luis Lozano Cervera. "Mathematical optimization for economic agents." Revista Ciencia y Tecnología 17, no. 3 (2021): 81–89. http://dx.doi.org/10.17268/rev.cyt.2021.03.07.

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6

Sezer, Ali Devin, and Gerhard-Wilhelm Weber. "Optimization Methods in Mathematical Finance." Optimization 62, no. 11 (2013): 1399–402. http://dx.doi.org/10.1080/02331934.2013.863528.

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7

Stanojević, Milan, and Bogdana Stanojević. "Lua APIs for mathematical optimization." Procedia Computer Science 242 (2024): 460–65. http://dx.doi.org/10.1016/j.procs.2024.08.160.

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8

García, J. M., C. A. Acosta, and M. J. Mesa. "Genetic algorithms for mathematical optimization." Journal of Physics: Conference Series 1448 (January 2020): 012020. http://dx.doi.org/10.1088/1742-6596/1448/1/012020.

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9

Gorissen, Bram L., Jan Unkelbach, and Thomas R. Bortfeld. "Mathematical Optimization of Treatment Schedules." International Journal of Radiation Oncology*Biology*Physics 96, no. 1 (2016): 6–8. http://dx.doi.org/10.1016/j.ijrobp.2016.04.012.

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10

Feichtinger, Gustav. "Mathematical Optimization and Economic Analysis." European Journal of Operational Research 221, no. 1 (2012): 273–74. http://dx.doi.org/10.1016/j.ejor.2012.03.018.

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11

Carrizosa, Emilio, and Dolores Romero Morales. "Supervised classification and mathematical optimization." Computers & Operations Research 40, no. 1 (2013): 150–65. http://dx.doi.org/10.1016/j.cor.2012.05.015.

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12

Blaydа, I. A. "OPTIMIZATION OF THE COAL BACTERIAL DESULFURIZATION USING MATHEMATICAL METHODS." Biotechnologia Acta 11, no. 6 (2018): 55–66. http://dx.doi.org/10.15407/biotech11.06.055.

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13

Mamadaev, Ibragim, and Alina Minitaeva. "Mathematical modeling of neural network compression techniques for mobile platforms." Journal of Physics: Conference Series 3042, no. 1 (2025): 012003. https://doi.org/10.1088/1742-6596/3042/1/012003.

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Abstract Mathematical modeling plays a critical role in the optimization of neural networks, particularly when adapting machine learning algorithms for resource-constrained mobile platforms. This paper explores neural network compression techniques, including model quantization, pruning, and computational graph optimization, through the lens of mathematical modeling. By developing formal representations of these methods, we analyze their impact on performance metrics such as execution speed, memory usage, and accuracy on mobile devices. Additionally, we examine how mathematical frameworks can

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14

BOBYLEV, A. I. "INTEGER OPTIMIZATION PROBLEM." Vestnik LSTU, no. 1 (2024): 30–37. http://dx.doi.org/10.53015/23049235_2024_1_30.

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Mathematical programming is a mathematical discipline that studies various extremal problems and develops algorithms for solving them. Among the problems are those of mathematical integer programming. Integer programming is indispensable for solving mathematical programming problems in which some or all of the variables take on integer values. The problems include the transport problem. The paper considers the transport problem and three linear programming methods for solving it: the method of potentials, the method of differential rents and the simplex method. The paper analyzes and assesses

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15

Yogita D. Bhise. "Mathematical Optimization of Electric Motor Designs." Panamerican Mathematical Journal 35, no. 1s (2024): 220–30. http://dx.doi.org/10.52783/pmj.v35.i1s.2310.

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When designing and improving the performance of electric motors, mathematical optimization is very important. The main goals are usually to make the motors more efficient, lower their costs, and work within certain limits. The main topic of this study is how to improve electric motor designs using advanced optimization methods, such as multi-objective optimization. The study looks at how to use different types of computer algorithms together, like gradient-based methods, genetic algorithms, and particle swarm optimization, to solve difficult design problems. Some of these problems are reducing

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16

Stanimirović, Predrag S., and Artem Stupin. "Dynamic programming in package Mathematica." ITM Web of Conferences 72 (2025): 01001. https://doi.org/10.1051/itmconf/20257201001.

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Dynamic programming (DP) is a powerful algorithmic technique for solving optimization problems by breaking them down into simpler subproblems. This paper presents an implementation of DP algorithms for two classic optimization problems: the Knapsack problem and the Traveling Salesman Problem (TSP). The solutions are developed and demonstrated using the Mathematica® programming language. For the Knapsack problem, we present two variants: with and without item repetition. The paper describes the mathematical formulation of each variant and provides detailed Mathematica code for their implementat

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17

Xie, Hua Long, Hui Min Guo, Qing Bao Wang, and Yong Xian Liu. "The Spindle Structural Optimization Design of HTC3250µn NC Machine Tool Based on ANSYS." Advanced Materials Research 457-458 (January 2012): 60–64. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.60.

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The optimization of spindle has important significance. The optimization method based on ANSYS is introduced and spindle mathematical mode of HTC3250µn NC machine tool is given. By scanning of design variables, the main optimized design variables are determined. The single objective and multi-objective optimizations are done. In the end, the main size comparison of spindle before and after optimization is given.

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18

Ulitinas, Tomas, and Stanislovas Kalanta. "OPTIMIZATION OF TRUSS HEIGHT." Mokslas - Lietuvos ateitis 2, no. 6 (2010): 56–60. http://dx.doi.org/10.3846/mla.2010.112.

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The article analyzes the task in truss height and in the optimization of the cross-sections of their elements. Element cross-sections are designed of steel profiles considering requirements for strength, stability and rigidity. A mathematical model is formulated as a nonlinear mathematical programming problem. It is solved as an iterative process, using mathematical software package “MATLAB” routine “fmincon”. The ratio of buckling is corrected in the each iteration. Optimization results are compared with those obtained applying software package “Robot Millennium”.

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19

Freeman, T. L., and Melvyn W. Jeter. "Mathematical Programming: An Introduction to Optimization." Mathematical Gazette 71, no. 458 (1987): 350. http://dx.doi.org/10.2307/3617112.

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20

Gozuyilmaz, Seyma, and O. Erhun Kundakcioglu. "Mathematical optimization for time series decomposition." OR Spectrum 43, no. 3 (2021): 733–58. http://dx.doi.org/10.1007/s00291-021-00637-w.

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21

Lucatero, Carlos Rodríguez, Marcelo Olivera Villaroel, and Paola Ovando. "A Mathematical Model for Agroforestry Optimization." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 17 (March 2, 2022): 108–22. http://dx.doi.org/10.37394/23203.2022.17.13.

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In the present article, we will describe some extensions of an agroforestry model that has been proposed and computationally implemented in [7]. Our generalizations consist of the inclusion of two additional species of tree, one culture, and a declaration of regeneration tours as variables definable by us as a parameter and the weight allocation by rentability of the treeless soil utilization as well as an exhaustive exploration of the different soil utilization scenarios in order to obtain the one who gives the best economic performance.

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22

McMullen, P. "MATHEMATICAL PROGRAMMING An Introduction to Optimization." Bulletin of the London Mathematical Society 19, no. 3 (1987): 290–91. http://dx.doi.org/10.1112/blms/19.3.290.

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23

Naidu, N. V. R. "Mathematical model for quality cost optimization." Robotics and Computer-Integrated Manufacturing 24, no. 6 (2008): 811–15. http://dx.doi.org/10.1016/j.rcim.2008.03.018.

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24

Zomorrodi, Ali R., Patrick F. Suthers, Sridhar Ranganathan, and Costas D. Maranas. "Mathematical optimization applications in metabolic networks." Metabolic Engineering 14, no. 6 (2012): 672–86. http://dx.doi.org/10.1016/j.ymben.2012.09.005.

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25

Rakesh, Vineet, and Ashim Datta. "Microwave puffing: mathematical modeling and optimization." Procedia Food Science 1 (2011): 762–69. http://dx.doi.org/10.1016/j.profoo.2011.09.115.

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26

Hales, Roland Oliver, and Sergio García. "Congress seat allocation using mathematical optimization." TOP 27, no. 3 (2019): 426–55. http://dx.doi.org/10.1007/s11750-019-00515-3.

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27

Liou, Y. C., X. Q. Yang, and J. C. Yao. "Mathematical Programs with Vector Optimization Constraints." Journal of Optimization Theory and Applications 126, no. 2 (2005): 345–55. http://dx.doi.org/10.1007/s10957-005-4720-4.

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28

Shah, Nita, and Poonam Mishra. "Oil production optimization: a mathematical model." Journal of Petroleum Exploration and Production Technology 3, no. 1 (2012): 37–42. http://dx.doi.org/10.1007/s13202-012-0040-z.

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29

Billionnet, Alain. "Mathematical optimization ideas for biodiversity conservation." European Journal of Operational Research 231, no. 3 (2013): 514–34. http://dx.doi.org/10.1016/j.ejor.2013.03.025.

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30

Venkayya, V. B. "Mathematical optimization in multi-disciplinary design." Mathematical and Computer Modelling 14 (1990): 29–36. http://dx.doi.org/10.1016/0895-7177(90)90144-c.

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31

Russenschuck, S., and T. Tortschanoff. "Mathematical optimization of superconducting accelerator magnets." IEEE Transactions on Magnetics 30, no. 5 (1994): 3419–22. http://dx.doi.org/10.1109/20.312673.

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32

Sakhapov, R. L., R. V. Nikolaeva, M. H. Gatiyatullin, and M. M. Makhmutov. "Mathematical model of highways network optimization." Journal of Physics: Conference Series 936 (December 2017): 012032. http://dx.doi.org/10.1088/1742-6596/936/1/012032.

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33

Şen, Alper, Kamyar Kargar, Esma Akgün, and Mustafa Ç. Pınar. "Codon optimization: a mathematical programing approach." Bioinformatics 36, no. 13 (2020): 4012–20. http://dx.doi.org/10.1093/bioinformatics/btaa248.

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Abstract Motivation Synthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software

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34

Heyer, Laurie J. "A Mathematical Optimization Problem in Bioinformatics." PRIMUS 18, no. 1 (2008): 101–18. http://dx.doi.org/10.1080/10511970701744992.

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35

Zowe, Jochem, Michal Kočvara, and Martin P. Bendsøe. "Free material optimization via mathematical programming." Mathematical Programming 79, no. 1-3 (1997): 445–66. http://dx.doi.org/10.1007/bf02614328.

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36

Norkin, B. V. "Mathematical models for insurance business optimization." Cybernetics and Systems Analysis 47, no. 1 (2011): 117–33. http://dx.doi.org/10.1007/s10559-011-9295-5.

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37

Ahmad Mala, Firdous. "Mathematical Analysis and Optimization for Economists." Technometrics 65, no. 2 (2023): 300–301. http://dx.doi.org/10.1080/00401706.2023.2201131.

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38

Petridis, Konstantinos, Garyfallos Arabatzis, and Angelo Sifaleras. "Mathematical optimization models for fuelwood production." Annals of Operations Research 294, no. 1-2 (2017): 59–74. http://dx.doi.org/10.1007/s10479-017-2697-7.

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39

Journal, IJSREM. "Reviewing the Role of Mathematical Optimization in Operations Research: Algorithms, Applications, and Challenges." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem28578.

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This review paper examines the pivotal role of mathematical optimization in operations research, focusing on its algorithms, applications, and challenges. Mathematical optimization, a cornerstone of operations research, offers powerful tools for addressing complex decision-making problems. We discuss a variety of optimization algorithms, from classical methods like linear programming to modern metaheuristic techniques such as genetic algorithms. Through specific case studies, we highlight the diverse applications of mathematical optimization in industries such as logistics, finance, and manufa

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40

Monabbati, S. E., and H. Torabi. "Mathematical modeling of finite topologies." Carpathian Mathematical Publications 12, no. 2 (2020): 434–42. http://dx.doi.org/10.15330/cmp.12.2.434-442.

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Integer programming is a tool for solving some combinatorial optimization problems. In this paper, we deal with combinatorial optimization problems on finite topologies. We use the binary representation of the sets to characterize finite topologies as the solutions of a Boolean quadratic system. This system is used as a basic model for formulating other types of topologies (e.g. door topology and $T_0$-topology) and some combinatorial optimization problems on finite topologies. As an example of the proposed model, we found that the smallest number $m(k)$ for which the topology exists on an $m(

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41

Zhang, Hongxin. "Optimization Strategies for Mathematical Algorithms in Computer Programming." Journal of Big Data and Computing 1, no. 1 (2023): 16–19. http://dx.doi.org/10.62517/jbdc.202301104.

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Computer programming is an important part of computing information technology, mathematical operation is one of the main modules of computer programming, through the optimization of mathematical operation to simple computer programming algorithm, can improve the efficiency of computer software. Therefore, in order to improve the efficiency of computer operation, it is particularly important to optimize the mathematical algorithms. Based on this, this paper studies the optimization strategy of mathematical algorithm in computer programming. Firstly, a brief overview of mathematical algorithm an

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42

Ma, Yunpeng, Xiaolu Wang, and Wanting Meng. "A Reinforced Whale Optimization Algorithm for Solving Mathematical Optimization Problems." Biomimetics 9, no. 9 (2024): 576. http://dx.doi.org/10.3390/biomimetics9090576.

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The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, and has been used to solve various practical optimization problems. In order to improve its convergence speed and solution quality, a reinforced whale optimization algorithm (RWOA) was designed. Firstly, an opposition-based learning strategy is used to generate other optima based on the best optimal solution found during the algorithm’s iteration, which can increase the diversity of the optimal solution and accelerate the convergence

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43

Gupta, Arnav, Ananya Sharma, Chen Li Wei, and Mehta Ravi. "Integrating Evolutionary Algorithms and Mathematical Modeling for Efficient Neural Network Optimization." Advances in Machine Learning & Artificial Intelligence 5, no. 4 (2024): 01–06. https://doi.org/10.33140/amlai.05.04.01.

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Optimizing neural network architectures presents significant challenges due to the vast search spaces and computational costs involved. This study explores the integration of evolutionary algorithms (EAs) and mathematical modeling techniques to enhance neural network optimization. We propose a novel framework combining EAs with dimensionality reduction, surrogate modeling, and hybrid optimization strategies to reduce computational complexity and improve performance. Our results demonstrate that the adapted EAs significantly increase accuracy and F1-scores while reducing the number of generatio

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44

J.O., Ekeocha Rowland. "Optimization of Systems." International Journal of Sciences Volume 8, no. 2019-03 (2019): 118–25. https://doi.org/10.5281/zenodo.3350615.

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Optimization is defined as the mathematical procedures involved in effecting optimality. It is also a collection of mathematical principles and methods used for solving quantitative problems in many disciplines. In business, optimization is finding an alternative with the most cost effective or highest achievable performance under given constraints by maximizing desired factors and minimizing undesired ones. The purpose of optimization is therefore to achieve the best design relative to a set of prioritized criteria or constraints which include maximizing factors such as productivity, strength

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45

Zhong, Mei Peng. "Parameter Optimization of Compressor Based on an Ant Colony Optimization." Applied Mechanics and Materials 201-202 (October 2012): 916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.916.

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A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pres

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46

Zekić, Ana. "Mathematical Optimization in Machine Learning for Computational Chemistry." Computation 13, no. 7 (2025): 169. https://doi.org/10.3390/computation13070169.

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Machine learning (ML) is transforming computational chemistry by accelerating molecular simulations, property prediction, and inverse design. Central to this transformation is mathematical optimization, which underpins nearly every stage of model development, from training neural networks and tuning hyperparameters to navigating chemical space for molecular discovery. This review presents a structured overview of optimization techniques used in ML for computational chemistry, including gradient-based methods (e.g., SGD and Adam), probabilistic approaches (e.g., Monte Carlo sampling and Bayesia

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47

Kutova, О. V., and R. V. Sahaidak-Nikitiuk. "Optimization methods for multi-criteria decisions in pharmacy." Social Pharmacy in Health Care 9, no. 4 (2023): 3–10. http://dx.doi.org/10.24959/sphhcj.23.302.

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Optimization methods for multi-criteria decisions in pharmacy In pharmaceutical technological research, the determination of the quantitative composition of granules is considered as a task of multi-criteria selection. Today, to solve this problem, the regression analysis and multi-criteria optimization methods are widely used; they are based on mathematical models obtained for the object under study. Aim. To identify a decision-making method in a multi-criteria space that is effective for use in pharmaceutical technology research with quantitative factors. Materials and meth

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48

Montoro, Johnny Moisés Valverde, Milton Milciades Cortez Gutiérrez, and Hernán Oscar Cortez Gutiérrez. "Optimization of the mathematical programming and applications." South Florida Journal of Development 2, no. 5 (2021): 7902–11. http://dx.doi.org/10.46932/sfjdv2n5-114.

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The present investigation responds to the need to solve optimization problems with optimality conditions. The KKT conditions are considered for multiobjective optimization problems with interval-valued objective functions.

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49

Lebedev, Vladimir, and Ekaterina Yushkova. "Mathematical model for optimization of heat exchange systems." E3S Web of Conferences 164 (2020): 02011. http://dx.doi.org/10.1051/e3sconf/202016402011.

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The article is devoted to the issue of thermodynamic optimization of heat transfer systems. Optimization is carried out by an exergy pinch method. This method includes the advantages of exergy analysis and pinch method. Exergy analysis takes into account the quantitative and qualitative characteristics of thermal processes, the pinch method allows structural and parametric optimization of heat transfer systems. The article presents a mathematical model for optimization by exergy pinch analysis. This model allows automated system optimization. Exergy pinch analysis allows more efficient use of

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50

Carrizosa, Emilio, Cristina Molero-Río, and Dolores Romero Morales. "Mathematical optimization in classification and regression trees." TOP 29, no. 1 (2021): 5–33. http://dx.doi.org/10.1007/s11750-021-00594-1.

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AbstractClassification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as

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