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Artigos de revistas sobre o tema "Infeasibility"

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1

Schweikard, Achim, e Fabian Schwarzer. "Detecting geometric infeasibility". Artificial Intelligence 105, n.º 1-2 (outubro de 1998): 139–59. http://dx.doi.org/10.1016/s0004-3702(98)00076-9.

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2

Artemov, Sergei, e Roman Kuznets. "Logical omniscience as infeasibility". Annals of Pure and Applied Logic 165, n.º 1 (janeiro de 2014): 6–25. http://dx.doi.org/10.1016/j.apal.2013.07.003.

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3

GREENBERG, HARVEY J. "Diagnosing Infeasibility in Min-cast Network Flow Problems Part I: Dual Infeasibility". IMA Journal of Management Mathematics 1, n.º 2 (1986): 99–109. http://dx.doi.org/10.1093/imaman/1.2.99.

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4

GREENBERG, HARVEY J. "Diagnosing Infeasibility in Min-cost Network Flow Problems Part II: Primal Infeasibility". IMA Journal of Management Mathematics 2, n.º 1 (1988): 39–50. http://dx.doi.org/10.1093/imaman/2.1.39.

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5

Kämpke, Thomas. "The geometry of linear infeasibility". Applied Mathematics and Computation 129, n.º 2-3 (julho de 2002): 317–37. http://dx.doi.org/10.1016/s0096-3003(01)00042-x.

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6

Cechlárová, Katarína, e Pavel Dikoxe. "Resolving infeasibility in extremal algebras". Linear Algebra and its Applications 290, n.º 1-3 (março de 1999): 267–73. http://dx.doi.org/10.1016/s0024-3795(98)10248-3.

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7

Almeida, Euclides, e Argimiro R. Secchi. "Solving dynamic optimization infeasibility problems". Computers & Chemical Engineering 36 (janeiro de 2012): 227–46. http://dx.doi.org/10.1016/j.compchemeng.2011.07.003.

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8

Liu, Minghui, e Gábor Pataki. "Exact duals and short certificates of infeasibility and weak infeasibility in conic linear programming". Mathematical Programming 167, n.º 2 (10 de abril de 2017): 435–80. http://dx.doi.org/10.1007/s10107-017-1136-5.

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9

Andersen, Kent, Quentin Louveaux e Robert Weismantel. "Certificates of linear mixed integer infeasibility". Operations Research Letters 36, n.º 6 (novembro de 2008): 734–38. http://dx.doi.org/10.1016/j.orl.2008.08.003.

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10

Chinneck, John W. "MINOS(IIS): Infeasibility analysis using MINOS". Computers & Operations Research 21, n.º 1 (janeiro de 1994): 1–9. http://dx.doi.org/10.1016/0305-0548(94)90057-4.

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11

Fendl, Hannes, Arnold Neumaier e Hermann Schichl. "Certificates of infeasibility via nonsmooth optimization". Journal of Global Optimization 69, n.º 1 (28 de outubro de 2016): 157–82. http://dx.doi.org/10.1007/s10898-016-0473-x.

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12

Fischetti, Matteo, e Andrea Lodi. "Repairing MIP infeasibility through local branching". Computers & Operations Research 35, n.º 5 (maio de 2008): 1436–45. http://dx.doi.org/10.1016/j.cor.2006.08.004.

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13

Atlihan, Mustafa K., e Linus Schrage. "Generalized filtering algorithms for infeasibility analysis". Computers & Operations Research 35, n.º 5 (maio de 2008): 1446–64. http://dx.doi.org/10.1016/j.cor.2006.08.005.

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14

Yang, Jian. "Infeasibility resolution based on goal programming". Computers & Operations Research 35, n.º 5 (maio de 2008): 1483–93. http://dx.doi.org/10.1016/j.cor.2006.08.006.

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15

Chakravarti, Nilotpal. "Some results concerning post-infeasibility analysis". European Journal of Operational Research 73, n.º 1 (fevereiro de 1994): 139–43. http://dx.doi.org/10.1016/0377-2217(94)90152-x.

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16

YANG, WEI, LIUSHENG HUANG, FANG SONG e QIYAN WANG. "INFEASIBILITY OF QUANTUM CRYPTOGRAPHY WITHOUT EAVESDROPPING CHECK". International Journal of Modern Physics B 25, n.º 08 (30 de março de 2011): 1061–67. http://dx.doi.org/10.1142/s0217979211058407.

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Secure key distribution is impossible in pure classical environment. Unconditional secure key distribution is available when quantum means are introduced, assisted by a classical communication channel. What is possible when a quantum key distribution scheme is without classical communication? We present a general model with this constraint and show that quantum key distribution without classical eavesdropping check is in principle impossible. For an adversary can always succeed in obtaining the secret key via a special case of man-in-the-middle attack, namely intercept-and-forward attack without any risk of being captured.
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17

Sawanobori, Chie. "Influence of fundraising infeasibility on accounting changes". Journal of Accounting & Organizational Change 9, n.º 4 (28 de outubro de 2013): 471–89. http://dx.doi.org/10.1108/jaoc-08-2010-0038.

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18

Xu, Zheng, Jian Yang e Nengjin Sheng. "Infeasibility Analysis of Half-Wavelength Transmission Systems". Energies 11, n.º 7 (8 de julho de 2018): 1790. http://dx.doi.org/10.3390/en11071790.

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19

Caraffini, Fabio, Anna V. Kononova e David Corne. "Infeasibility and structural bias in differential evolution". Information Sciences 496 (setembro de 2019): 161–79. http://dx.doi.org/10.1016/j.ins.2019.05.019.

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20

Song, Yingbo, Michael E. Locasto, Angelos Stavrou, Angelos D. Keromytis e Salvatore J. Stolfo. "On the infeasibility of modeling polymorphic shellcode". Machine Learning 81, n.º 2 (29 de outubro de 2009): 179–205. http://dx.doi.org/10.1007/s10994-009-5143-5.

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21

Timminga, Ellen. "Solving Infeasibility Problems in Computerized Test Assembly". Applied Psychological Measurement 22, n.º 3 (setembro de 1998): 280–91. http://dx.doi.org/10.1177/01466216980223008.

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22

Applegate, David L., William Cook e S. Thomas McCormick. "Integral infeasibility and testing total dual integrality". Operations Research Letters 10, n.º 1 (fevereiro de 1991): 37–41. http://dx.doi.org/10.1016/0167-6377(91)90084-3.

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23

Lourenço, Bruno F., Masakazu Muramatsu e Takashi Tsuchiya. "Weak infeasibility in second order cone programming". Optimization Letters 10, n.º 8 (24 de dezembro de 2015): 1743–55. http://dx.doi.org/10.1007/s11590-015-0982-4.

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24

Lee, Hsuan-Shih, Gwo-Hshiung Tzeng, Weichung Yeih, Yu-Jie Wang e Shing-Chih Yang. "Revised DEMATEL: Resolving the Infeasibility of DEMATEL". Applied Mathematical Modelling 37, n.º 10-11 (junho de 2013): 6746–57. http://dx.doi.org/10.1016/j.apm.2013.01.016.

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25

Zhang, Jianmin, Shengyu Shen, Jun Zhang, Weixia Xu e LI Sikun. "Extracting minimal unsatisfiable subformulas in satisfiability modulo theories". Computer Science and Information Systems 8, n.º 3 (2011): 693–710. http://dx.doi.org/10.2298/csis101019024z.

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Explaining the causes of infeasibility of formulas has practical applications in various fields, such as formal verification and electronic design automation. A minimal unsatisfiable subformula provides a succinct explanation of infeasibility and is valuable for applications. The problem of deriving minimal unsatisfiable cores from Boolean formulas has been addressed rather frequently in recent years. However little attention has been concentrated on extraction of unsatisfiable subformulas in Satisfiability Modulo Theories(SMT). In this paper, we propose a depth-firstsearch algorithm and a breadth-first-search algorithm to compute minimal unsatisfiable cores in SMT, adopting different searching strategy. We report and analyze experimental results obtaining from a very extensive test on SMT-LIB benchmarks.
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26

Guo, Dong, e Zheng-Qun Cai. "Super-Efficiency Infeasibility in the Presence of Nonradial Measurement". Mathematical Problems in Engineering 2020 (19 de setembro de 2020): 1–7. http://dx.doi.org/10.1155/2020/6264852.

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The original radial VRS super-efficiency model in DEA excludes the DMU under evaluation from the reference set. However, it must lead to the problem of infeasibility specifically when the DMU under consideration is at the extremity of the frontier. In this paper, a modified nonradial VRS super-efficiency model is established. The super-efficiency model in the presence of nonradial measurement still maintains some good properties, and the original radial VRS super-efficiency model infeasibility can also be detected through it. Our model with nonradial measurement can help decision makers allocate input resources and arrange production activities because it finds an efficient benchmark DMU, which is different from the reference DMU under radial measurement.
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27

Obuchowska, Wieslawa T. "Infeasibility analysis for systems of quadratic convex inequalities". European Journal of Operational Research 107, n.º 3 (junho de 1998): 633–43. http://dx.doi.org/10.1016/s0377-2217(97)00168-9.

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28

Hsiao-Fan Wang e Hsueh-Ling Liao. "Fuzzy resolution on the infeasibility of variational inequality". European Journal of Operational Research 106, n.º 1 (abril de 1998): 198–203. http://dx.doi.org/10.1016/s0377-2217(98)00312-9.

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29

Vada, Jostein, Olav Slupphaug, Tor Arne Johansen e Bjarne A. Foss. "Stabilizing Linear MPC with Efficient Prioritized Infeasibility Handling". IFAC Proceedings Volumes 33, n.º 10 (junho de 2000): 299–304. http://dx.doi.org/10.1016/s1474-6670(17)38556-7.

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30

Cook, W. D., L. Liang, Y. Zha e J. Zhu. "A modified super-efficiency DEA model for infeasibility". Journal of the Operational Research Society 60, n.º 2 (fevereiro de 2009): 276–81. http://dx.doi.org/10.1057/palgrave.jors.2602544.

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31

Denning, Karen C., Stephen P. Ferris e Robert M. Lawless. "Serial bankruptcy: plan infeasibility or just bad luck?" Applied Economics Letters 8, n.º 2 (fevereiro de 2001): 105–9. http://dx.doi.org/10.1080/13504850150204156.

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32

Byrd, Richard H., Frank E. Curtis e Jorge Nocedal. "Infeasibility Detection and SQP Methods for Nonlinear Optimization". SIAM Journal on Optimization 20, n.º 5 (janeiro de 2010): 2281–99. http://dx.doi.org/10.1137/080738222.

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33

Bielschowsky, Roberto H., e Francisco A. M. Gomes. "Dynamic Control of Infeasibility in Equality Constrained Optimization". SIAM Journal on Optimization 19, n.º 3 (janeiro de 2008): 1299–325. http://dx.doi.org/10.1137/070679557.

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34

Obuchowska, Wiesława T. "On Infeasibility of Systems of Convex Analytic Inequalities". Journal of Mathematical Analysis and Applications 234, n.º 1 (junho de 1999): 223–45. http://dx.doi.org/10.1006/jmaa.1999.6357.

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35

Chan, Nathan. "Partial Infeasibility Method for Chance‐Constrained Aquifer Management". Journal of Water Resources Planning and Management 120, n.º 1 (janeiro de 1994): 70–89. http://dx.doi.org/10.1061/(asce)0733-9496(1994)120:1(70).

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36

Seiford, Lawrence M., e Joe Zhu. "Infeasibility Of Super-Efficiency Data Envelopment Analysis Models". INFOR: Information Systems and Operational Research 37, n.º 2 (maio de 1999): 174–87. http://dx.doi.org/10.1080/03155986.1999.11732379.

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37

Bordetsky, Alexander B. "Reasoning on infeasibility in distributed collaborative computing environment". Annals of Mathematics and Artificial Intelligence 17, n.º 1 (março de 1996): 155–76. http://dx.doi.org/10.1007/bf02284629.

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38

Lee, Hsuan-Shih, Ching-Wu Chu e Joe Zhu. "Super-efficiency DEA in the presence of infeasibility". European Journal of Operational Research 212, n.º 1 (julho de 2011): 141–47. http://dx.doi.org/10.1016/j.ejor.2011.01.022.

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39

Lee, Hsuan-Shih, e Joe Zhu. "Super-efficiency infeasibility and zero data in DEA". European Journal of Operational Research 216, n.º 2 (janeiro de 2012): 429–33. http://dx.doi.org/10.1016/j.ejor.2011.07.050.

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40

Takemura, Akimichi, e Ruriko Yoshida. "A generalization of the integer linear infeasibility problem". Discrete Optimization 5, n.º 1 (fevereiro de 2008): 36–52. http://dx.doi.org/10.1016/j.disopt.2007.10.004.

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41

Estellita Lins, M. P., A. C. Moreira da Silva e C. A. K. Lovell. "Avoiding infeasibility in DEA models with weight restrictions". European Journal of Operational Research 181, n.º 2 (setembro de 2007): 956–66. http://dx.doi.org/10.1016/j.ejor.2006.05.041.

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42

Puranik, Yash, Apurva Samudra, Nikolaos V. Sahinidis, Alexander B. Smith e Bijan Sayyar-Rodsari. "Infeasibility resolution for multi-purpose batch process scheduling". Computers & Chemical Engineering 116 (agosto de 2018): 69–79. http://dx.doi.org/10.1016/j.compchemeng.2018.03.005.

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43

Tang, Jing, Jianzhong Liu, Jianghua Chen e Fangqing Wei. "Performance Ranking Method Based on Superefficiency with Directional Distance Function in DEA". Mathematical Problems in Engineering 2020 (18 de fevereiro de 2020): 1–7. http://dx.doi.org/10.1155/2020/2458343.

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In data envelopment analysis (DEA) methodology, superefficiency models eliminate the DMU to be evaluated from the production possibility set (PPS) to investigate whether its performance is superefficient. However, the infeasibility has been found in the superefficiency models when variable return-to-scale (VRS) technology is assumed. In recent developments, directional distance functions (DDF) are introduced into VRS superefficiency models to address the infeasibility, and the obtained efficiency scores from the DDF-based VRS superefficiency measure are used to rank all DMUs. In this study, we discuss conditions on selecting some proper reference bundles for feasible DDF and suggest a new DDF-based VRS superefficiency measure, which is unit-invariant and does not need to specify additional parameters. Two example illustrations are evaluated to demonstrate the feasibility and usefulness of our proposed DDF-based VRS superefficiency ranking method.
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44

Jain, Dr Leena, e Mr Amit Bhanot. "Traveling Salesman Problem: A Case Study". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, n.º 1 (1 de agosto de 2012): 167–69. http://dx.doi.org/10.24297/ijct.v3i1c.2773.

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In this paper assignment based integer linear formulation presented for solving traveling salesman problem. Unfortunately, the assignment model can lead to infeasible solutions. Infeasibility removes by introducing additional constraints. Then this linear problem solved by open source software.
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45

Matacic, Stanislav. "CYBERTHERAPY: ON FEASIBILITY AND INFEASIBILITY OF REMOTE PSYCHOANALYTIC THERAPIES". Psihoterapija 32, n.º 2 (5 de fevereiro de 2019): 195–215. http://dx.doi.org/10.24869/psihei.2018.195.

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46

Wagler, Annegret K., e Jan-Thierry Wegener. "Preprocessing for Network Reconstruction: Feasibility Test and Handling Infeasibility". Fundamenta Informaticae 135, n.º 4 (2014): 521–35. http://dx.doi.org/10.3233/fi-2014-1138.

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47

De Loera, Jesús A., Jon Lee, Peter N. Malkin e Susan Margulies. "Computing infeasibility certificates for combinatorial problems through Hilbert’s Nullstellensatz". Journal of Symbolic Computation 46, n.º 11 (novembro de 2011): 1260–83. http://dx.doi.org/10.1016/j.jsc.2011.08.007.

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48

Singh, Hemant Kumar, Tapabrata Ray e Ruhul Sarker. "Optimum Oil Production Planning Using Infeasibility Driven Evolutionary Algorithm". Evolutionary Computation 21, n.º 1 (março de 2013): 65–82. http://dx.doi.org/10.1162/evco_a_00064.

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In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil recovery (EOR). The total gas that can be used for oil extraction is constrained by daily availability limits. The oil extracted from each well is known to be a nonlinear function of the gas injected into the well and varies between wells. The problem is to identify the optimal amount of gas that needs to be injected into each well to maximize the amount of oil extracted subject to the constraint on the total daily gas availability. The problem has long been of practical interest to all major oil exploration companies as it has the potential to derive large financial benefit. In this paper, an infeasibility driven evolutionary algorithm is used to solve a 56 well reservoir problem which demonstrates its efficiency in solving constrained optimization problems. Furthermore, a multi-objective formulation of the problem is posed and solved using a number of algorithms, which eliminates the need for solving the (single objective) problem on a regular basis. Lastly, a modified single objective formulation of the problem is also proposed, which aims to maximize the profit instead of the quantity of oil. It is shown that even with a lesser amount of oil extracted, more economic benefits can be achieved through the modified formulation.
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49

Puranik, Yash, e Nikolaos V. Sahinidis. "Deletion Presolve for Accelerating Infeasibility Diagnosis in Optimization Models". INFORMS Journal on Computing 29, n.º 4 (novembro de 2017): 754–66. http://dx.doi.org/10.1287/ijoc.2017.0761.

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50

Gohari, Amin, e Venkat Anantharam. "Infeasibility Proof and Information State in Network Information Theory". IEEE Transactions on Information Theory 60, n.º 10 (outubro de 2014): 5992–6004. http://dx.doi.org/10.1109/tit.2014.2347301.

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