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Thematische Bibliographien / Quadratic programmin / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Quadratic programmin“

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Veröffentlicht am 1. April 2023

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1

Majeed, Amir Sabir, und Fadhil Salman Abed. „A Proposed Method to Solve Quadratic Fractional Programming Problem by Converting to Double Linear Programming“. Journal of Zankoy Sulaimani - Part A 19, Nr. 1 (05.06.2016): 239–49. http://dx.doi.org/10.17656/jzs.10602.

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2

Yamashita, Hiroshi, und Hiroshige Dan. „GLOBAL CONVERGENCE OF A TRUST REGION SEQUENTIAL QUADRATIC PROGRAMMING METHOD“. Journal of the Operations Research Society of Japan 48, Nr. 1 (2005): 41–56. http://dx.doi.org/10.15807/jorsj.48.41.

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3

Turlach, Berwin A., und Stephen J. Wright. „Quadratic programming“. Wiley Interdisciplinary Reviews: Computational Statistics 7, Nr. 2 (19.01.2015): 153–59. http://dx.doi.org/10.1002/wics.1344.

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4

Serna-Diaz, Raquel, Raimundo Santos Leite und Paulo J. S. Silva. „A mixed quadratic programming model for a robust support vector machine“. Selecciones Matemáticas 8, Nr. 1 (30.06.2021): 27–36. http://dx.doi.org/10.17268/sel.mat.2021.01.03.

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5

Sulaiman, Nejmaddin A., und Maher A. Nawkhass. „Using Standard Division to Solve Multi- Objective Quadratic Fractional Programming Problems“. Journal of Zankoy Sulaimani - Part A 18, Nr. 3 (05.06.2016): 157–64. http://dx.doi.org/10.17656/jzs.10544.

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6

Sulaiman, Najmaddin A., und Basiya K. Abulrahim. „Arithmetic Average Transformation Technique to Solve Multi-Objective Quadratic Programming Problem“. Journal of Zankoy Sulaimani - Part A 15, Nr. 1 (17.12.2012): 57–69. http://dx.doi.org/10.17656/jzs.10233.

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7

Wilson, G. „Quadratic programming analogs“. IEEE Transactions on Circuits and Systems 33, Nr. 9 (September 1986): 907–11. http://dx.doi.org/10.1109/tcs.1986.1086021.

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8

Boggs, Paul T., und Jon W. Tolle. „Sequential Quadratic Programming“. Acta Numerica 4 (Januar 1995): 1–51. http://dx.doi.org/10.1017/s0962492900002518.

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Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved. Backed by a solid theoretical and computational foundation, both commercial and public-domain SQP algorithms have been developed and used to solve a remarkably large set of important practical problems. Recently large-scale versions have been devised and tested with promising results.

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9

Beck, Amir. „Quadratic Matrix Programming“. SIAM Journal on Optimization 17, Nr. 4 (Januar 2007): 1224–38. http://dx.doi.org/10.1137/05064816x.

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10

Koyuncu, Nursel. „Computation of Parameters Using Genetic Algorithm and Sequential Quadratic Programming in Sampling“. International Journal of Computer Theory and Engineering 7, Nr. 5 (Oktober 2015): 394–97. http://dx.doi.org/10.7763/ijcte.2015.v7.992.

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11

Rakshit, Madhuchanda, und Suchet Kumar. „A Bilevel Quadratic–Quadratic Fractional Programming through Fuzzy Goal Programming approach“. International Journal of Mathematics Trends and Technology 38, Nr. 3 (25.10.2016): 130–37. http://dx.doi.org/10.14445/22315373/ijmtt-v38p523.

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12

Abdulrahim, Basiya K. „Solving Quadratic Fractional Programming Problem via Feasible Direction Development and Modified Simplex Method“. Journal of Zankoy Sulaimani - Part A 15, Nr. 2 (04.03.2013): 45–52. http://dx.doi.org/10.17656/jzs.10245.

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13

Wu, Baiyi, Xiaoling Sun, Duan Li und Xiaojin Zheng. „Quadratic Convex Reformulations for Semicontinuous Quadratic Programming“. SIAM Journal on Optimization 27, Nr. 3 (Januar 2017): 1531–53. http://dx.doi.org/10.1137/15m1012232.

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14

De Leone, Renato. „Nonlinear programming and Grossone: Quadratic Programing and the role of Constraint Qualifications“. Applied Mathematics and Computation 318 (Februar 2018): 290–97. http://dx.doi.org/10.1016/j.amc.2017.03.029.

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15

Syaripuddin, Herry Suprajitno und Fatmawati. „Solution of Quadratic Programming with Interval Variables Using a Two-Level Programming Approach“. Journal of Applied Mathematics 2018 (30.07.2018): 1–7. http://dx.doi.org/10.1155/2018/5204375.

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Quadratic programming with interval variables is developed from quadratic programming with interval coefficients to obtain optimum solution in interval form, both the optimum point and optimum value. In this paper, a two-level programming approach is used to solve quadratic programming with interval variables. Procedure of two-level programming is transforming the quadratic programming model with interval variables into a pair of classical quadratic programming models, namely, the best optimum and worst optimum problems. The procedure to solve the best and worst optimum problems is also constructed to obtain optimum solution in interval form.

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16

Lau, Karen K., und Robert S. Womersley. „Multistage quadratic stochastic programming“. Journal of Computational and Applied Mathematics 129, Nr. 1-2 (April 2001): 105–38. http://dx.doi.org/10.1016/s0377-0427(00)00545-8.

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17

Gupta, Omprakash K. „Applications of Quadratic Programming“. Journal of Information and Optimization Sciences 16, Nr. 1 (Januar 1995): 177–94. http://dx.doi.org/10.1080/02522667.1995.10699213.

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18

Best, M. J., und K. Ritter. „A quadratic programming algorithm“. Zeitschrift für Operations Research 32, Nr. 5 (September 1988): 271–97. http://dx.doi.org/10.1007/bf01920297.

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19

Kumar, Suchet, und Madhuchanda Rakshit. „A Solution of Fuzzy Multilevel Quadratic Fractional Programming Problem through Interactive Fuzzy Goal Programming Approach“. International Journal of Fuzzy Mathematical Archive 13, Nr. 01 (2017): 83–97. http://dx.doi.org/10.22457/ijfma.v13n1a9.

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The purpose of this paper is to study the fuzzy multilevel quadratic fractional programming problem through fuzzy goal programming procedure. A fuzzy multilevel quadratic fractional programming problem is a type of hierarchical programming problem which contains fuzzy parameters as coefficients of cost in objective function, the resources and the technological coefficients. Here, we are considering those fuzzy parameters as the triangular fuzzy numbers. Firstly, we are transferring the fuzzy multilevel quadratic fractional programming problem into a deterministic multilevel multiobjective quadratic fractional programming problem by using Zadeh extension principle. Then, an interactive fuzzy goal programming procedure is used to solve this equivalent deterministic multiobjective multilevel quadratic fractional programming problem by using respective membership functions. An illustrative numerical example for fuzzy four level quadratic fractional programming problem is provided to reveal the practicability of the proposed method.

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20

Zhou, Xue-Gang, Bing-Yuan Cao und Seyed Hadi Nasseri. „Optimality Conditions for Fuzzy Number Quadratic Programming with Fuzzy Coefficients“. Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/489893.

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The purpose of the present paper is to investigate optimality conditions and duality theory in fuzzy number quadratic programming (FNQP) in which the objective function is fuzzy quadratic function with fuzzy number coefficients and the constraint set is fuzzy linear functions with fuzzy number coefficients. Firstly, the equivalent quadratic programming of FNQP is presented by utilizing a linear ranking function and the dual of fuzzy number quadratic programming primal problems is introduced. Secondly, we present optimality conditions for fuzzy number quadratic programming. We then prove several duality results for fuzzy number quadratic programming problems with fuzzy coefficients.

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21

Sulaiman, Nejmaddin A., Ronak M. Abdullah und Snur O. Abdull. „Using Optimal Geometric Average Technique to Solve Extreme Point Multi- Objective Quadratic Programming Problems“. Journal of Zankoy Sulaimani - Part A 18, Nr. 3 (28.01.2016): 63–72. http://dx.doi.org/10.17656/jzs.10535.

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22

Madsen, Kaj, Hans Bruun Nielsen und Mustafa Ç. Pınar. „Bound constrained quadratic programming via piecewise quadratic functions“. Mathematical Programming 85, Nr. 1 (01.05.1999): 135–56. http://dx.doi.org/10.1007/s101070050049.

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23

Ye, Yinyu. „Approximating quadratic programming with bound and quadratic constraints“. Mathematical Programming 84, Nr. 2 (Februar 1999): 219–26. http://dx.doi.org/10.1007/s10107980012a.

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24

Plateau, Marie-Christine. „Quadratic convex reformulations for quadratic 0–1 programming“. 4OR 6, Nr. 2 (30.05.2007): 187–90. http://dx.doi.org/10.1007/s10288-007-0044-6.

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25

Syaripuddin, Herry Suprajitno und Fatmawati. „Extension of Wolfe Method for Solving Quadratic Programming with Interval Coefficients“. Journal of Applied Mathematics 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/9037857.

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Quadratic programming with interval coefficients developed to overcome cases in classic quadratic programming where the coefficient value is unknown and must be estimated. This paper discusses the extension of Wolfe method. The extended Wolfe method can be used to solve quadratic programming with interval coefficients. The extension process of Wolfe method involves the transformation of the quadratic programming with interval coefficients model into linear programming with interval coefficients model. The next step is transforming linear programming with interval coefficients model into two classic linear programming models with special characteristics, namely, the optimum best and the worst optimum problem.

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26

MU, XUEWEN, SANYANG LID und YALING ZHANG. „A SUCCESSIVE QUADRATIC PROGRAMMING ALGORITHM FOR SDP RELAXATION OF THE BINARY QUADRATIC PROGRAMMING“. Bulletin of the Korean Mathematical Society 42, Nr. 4 (01.11.2005): 837–49. http://dx.doi.org/10.4134/bkms.2005.42.4.837.

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27

Etoa, Jean Bosco Etoa. „Solving convex quadratic bilevel programming problems using an enumeration sequential quadratic programming algorithm“. Journal of Global Optimization 47, Nr. 4 (04.11.2009): 615–37. http://dx.doi.org/10.1007/s10898-009-9482-3.

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28

Wang, Xiao Guang, und Chun Juan Hou. „An Entropy Function Implementation of Quadratic Programming“. Applied Mechanics and Materials 226-228 (November 2012): 2227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.2227.

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This paper is intended to describe a new algorithm, which makes entropy function method with Lagrange function and Taylor formula for solving inseparable variables of quadratic programming. Quadratic programming problem are an important in the fields of nonlinear programming problem. Entropy function also called KS function. The nature and related certificate of KS function and its convergence have already been proved at home and abroad. The application of KS function nature for solving quadratic programming is a very good method, and it is one of the advantages of making more constraint programming problem become a single constraint programming problem, and the original problems are simplified. Electing three examples of separated variables for quadratic programming problems, that is cross terms of zero, and then contrasted with the new method. The algorithm resolves implementation of separated variables of quadratic programming. Our numerical experiments show the proposed algorithm is feasible.

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29

Sugiyarto. „Alternative solution optimization quadratic form with fuzzy number parameters“. Journal of Physics: Conference Series 2279, Nr. 1 (01.05.2022): 012012. http://dx.doi.org/10.1088/1742-6596/2279/1/012012.

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Abstract The fuzzy number is one of the alternatives to maximize solving a quadratic model programming problem. Based on that statement, this paper provides one of the methods to solve the quadratic model programming problem. It started with a general discussion on quadratic model programming and continued by transposing a basic form into a fuzzy quadratic programming equation and giving a reference to solve that problem. Finally, a few examples are provided to analyse how accurate this method works.

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30

Prof. Sathish. „Nonlinear Quadratic Fractional Transportation Problem for Optimal Solution Deduction“. Journal of Soft Computing Paradigm 2, Nr. 2 (20.05.2020): 92–100. http://dx.doi.org/10.36548/jscp.2020.2.002.

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The manifold non-linear programming problems (NLPP) are dealt by people in their daily routines in the form of real time uses. The non-linear problem could deliver a remedies on the problems that require decision making, for instance corporate planning as well as finance, production and marketing, sales and inventory etc. this makes the fractional programing a research area of predominance. The fractional programming in transportation problem of disposing a one type of goods to various endpoint with varying quantities would enable to identify probable solution at a minimized cost and duration. The paper with the research study on the one such NLPP is coined as the fractional-quadratic transportation problem. (FQTP). The NLPP are highly popular since they deliver a supreme depictions of distribution problems for the real-life applications were the transportation cost remains changing. The proposed strides in the paper emphasis on deducing the solutions that are optimal for such difficulty. The proposed algorithm is examined with the numerical instance to demonstrate the proficiency of the algorithm and its benefits in the transportation structure belonging to different area of application

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31

Anstreicher, Kurt M., und Nathan W. Brixius. „Solving quadratic assignment problems using convex quadratic programming relaxations“. Optimization Methods and Software 16, Nr. 1-4 (Januar 2001): 49–68. http://dx.doi.org/10.1080/10556780108805828.

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32

Stetsyuk, P., O. Lykhovyd und A. Suprun. „On Linear and Quadratic Two-Stage Transportation Problem“. Cybernetics and Computer Technologies, Nr. 4 (31.12.2020): 5–14. http://dx.doi.org/10.34229/2707-451x.20.4.1.

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Introduction. When formulating the classical two-stage transportation problem, it is assumed that the product is transported from suppliers to consumers through intermediate points. Intermediary firms and various kinds of storage facilities (warehouses) can act as intermediate points. The article discusses two mathematical models for two-stage transportation problem (linear programming problem and quadratic programming problem) and a fairly universal way to solve them using modern software. It uses the description of the problem in the modeling language AMPL (A Mathematical Programming Language) and depends on which of the known programs is chosen to solve the problem of linear or quadratic programming. The purpose of the article is to propose the use of AMPL code for solving a linear programming two-stage transportation problem using modern software for linear programming problems, to formulate a mathematical model of a quadratic programming two-stage transportation problem and to investigate its properties. Results. The properties of two variants of a two-stage transportation problem are described: a linear programming problem and a quadratic programming problem. An AMPL code for solving a linear programming two-stage transportation problem using modern software for linear programming problems is given. The results of the calculation using Gurobi program for a linear programming two-stage transportation problem, which has many solutions, are presented and analyzed. A quadratic programming two-stage transportation problem was formulated and conditions were found under which it has unique solution. Conclusions. The developed AMPL-code for a linear programming two-stage transportation problem and its modification for a quadratic programming two-stage transportation problem can be used to solve various logistics transportation problems using modern software for solving mathematical programming problems. The developed AMPL code can be easily adapted to take into account the lower and upper bounds for the quantity of products transported from suppliers to intermediate points and from intermediate points to consumers. Keywords: transportation problem, linear programming problem, AMPL modeling language, Gurobi program, quadratic programming problem.

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33

Motee, N., und A. Jadbabaie. „Distributed Multi-Parametric Quadratic Programming“. IEEE Transactions on Automatic Control 54, Nr. 10 (Oktober 2009): 2279–89. http://dx.doi.org/10.1109/tac.2009.2014916.

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34

Smith, Andrew D. A. C. „Quadratic Programming and Penalized Regression“. Communications in Statistics - Theory and Methods 42, Nr. 7 (April 2013): 1363–72. http://dx.doi.org/10.1080/03610926.2012.732177.

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35

Shen, Junjie, und Dennis Hong. „Optimal Linearization via Quadratic Programming“. IEEE Robotics and Automation Letters 5, Nr. 3 (Juli 2020): 4572–79. http://dx.doi.org/10.1109/lra.2020.3002449.

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36

Gupta, Renu, und M. C. Puri. „Bicriteria integer quadratic programming problems“. Journal of Interdisciplinary Mathematics 3, Nr. 2-3 (Juni 2000): 133–48. http://dx.doi.org/10.1080/09720502.2000.10700277.

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37

Yuan, Ganzhao, Zhenjie Zhang, Bernard Ghanem und Zhifeng Hao. „Low-rank quadratic semidefinite programming“. Neurocomputing 106 (April 2013): 51–60. http://dx.doi.org/10.1016/j.neucom.2012.10.014.

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38

Canestrelli, Elio, Silvio Giove und Robert Fullér. „Stability in possibilistic quadratic programming“. Fuzzy Sets and Systems 82, Nr. 1 (August 1996): 51–56. http://dx.doi.org/10.1016/0165-0114(95)00267-7.

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39

Vavasis, Stephen A. „Quadratic programming is in NP“. Information Processing Letters 36, Nr. 2 (Oktober 1990): 73–77. http://dx.doi.org/10.1016/0020-0190(90)90100-c.

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40

Fletcher, R. „Resolving degeneracy in quadratic programming“. Annals of Operations Research 46-47, Nr. 2 (September 1993): 307–34. http://dx.doi.org/10.1007/bf02023102.

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41

Horváth, Á. P. „Potential theory and quadratic programming“. Bulletin des Sciences Mathématiques 160 (Mai 2020): 102841. http://dx.doi.org/10.1016/j.bulsci.2020.102841.

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42

Heinkenschloss, Matthias. „Projected Sequential Quadratic Programming Methods“. SIAM Journal on Optimization 6, Nr. 2 (Mai 1996): 373–417. http://dx.doi.org/10.1137/0806022.

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43

Xuewen, Mu, Zhang Yaling und Liu Sanyang. „Successive quadratic programming multiuser detector“. Journal of Systems Engineering and Electronics 18, Nr. 1 (März 2007): 8–13. http://dx.doi.org/10.1016/s1004-4132(07)60042-5.

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44

Lucia, A., und J. Xu. „Methods of successive quadratic programming“. Computers & Chemical Engineering 18 (Januar 1994): S211—S215. http://dx.doi.org/10.1016/0098-1354(94)80035-9.

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45

Chang, Shang-Wang. „A method for quadratic programming“. Naval Research Logistics Quarterly 33, Nr. 3 (August 1986): 479–87. http://dx.doi.org/10.1002/nav.3800330312.

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46

Ho, Diem. „Quadratic programming for portfolio optimization“. Applied Stochastic Models and Data Analysis 8, Nr. 3 (September 1992): 189–94. http://dx.doi.org/10.1002/asm.3150080308.

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47

Best, Michael J., und Jaroslava Hlouskova. „Quadratic programming with transaction costs“. Computers & Operations Research 35, Nr. 1 (Januar 2008): 18–33. http://dx.doi.org/10.1016/j.cor.2006.02.013.

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48

Izmailov, A. F., und E. I. Uskov. „Subspace-stabilized sequential quadratic programming“. Computational Optimization and Applications 67, Nr. 1 (05.01.2017): 129–54. http://dx.doi.org/10.1007/s10589-016-9890-5.

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49

Сальков und Nikolay Sal'kov. „Graph-analytic Solution of Some Special Problems of Quadratic Programming“. Geometry & Graphics 2, Nr. 1 (03.03.2014): 3–8. http://dx.doi.org/10.12737/3842.

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Quadratic programming problems are one of special cases of mathematical programming problems. Mathematical programming problems solution is of great importance, because these problems are those of optimizing of solution related to presented issues from multitude of possible ones. The mathematical programming problems are linear, nonlinear, dynamic and others. It is suggested to consider a graph-analytic solution of quadratic programming’s special problems, which, taken together, constitute the quadratic programming problems for two and three variables. A total of eight special problems have been considered.

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50

Mu, Xuewen, und Yaling Zhang. „A Rank-Two Feasible Direction Algorithm for the Binary Quadratic Programming“. Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/963563.

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Based on the semidefinite programming relaxation of the binary quadratic programming, a rank-two feasible direction algorithm is presented. The proposed algorithm restricts the rank of matrix variable to be two in the semidefinite programming relaxation and yields a quadratic objective function with simple quadratic constraints. A feasible direction algorithm is used to solve the nonlinear programming. The convergent analysis and time complexity of the method is given. Coupled with randomized algorithm, a suboptimal solution is obtained for the binary quadratic programming. At last, we report some numerical examples to compare our algorithm with randomized algorithm based on the interior point method and the feasible direction algorithm on max-cut problem. Simulation results have shown that our method is faster than the other two methods.

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