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Journal articles on the topic 'Infeasibility'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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1

Schweikard, Achim, and Fabian Schwarzer. "Detecting geometric infeasibility." Artificial Intelligence 105, no. 1-2 (October 1998): 139–59. http://dx.doi.org/10.1016/s0004-3702(98)00076-9.

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2

Artemov, Sergei, and Roman Kuznets. "Logical omniscience as infeasibility." Annals of Pure and Applied Logic 165, no. 1 (January 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, no. 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, no. 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, no. 2-3 (July 2002): 317–37. http://dx.doi.org/10.1016/s0096-3003(01)00042-x.

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6

Cechlárová, Katarína, and Pavel Dikoxe. "Resolving infeasibility in extremal algebras." Linear Algebra and its Applications 290, no. 1-3 (March 1999): 267–73. http://dx.doi.org/10.1016/s0024-3795(98)10248-3.

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7

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

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8

Liu, Minghui, and Gábor Pataki. "Exact duals and short certificates of infeasibility and weak infeasibility in conic linear programming." Mathematical Programming 167, no. 2 (April 10, 2017): 435–80. http://dx.doi.org/10.1007/s10107-017-1136-5.

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9

Andersen, Kent, Quentin Louveaux, and Robert Weismantel. "Certificates of linear mixed integer infeasibility." Operations Research Letters 36, no. 6 (November 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, no. 1 (January 1994): 1–9. http://dx.doi.org/10.1016/0305-0548(94)90057-4.

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11

Fendl, Hannes, Arnold Neumaier, and Hermann Schichl. "Certificates of infeasibility via nonsmooth optimization." Journal of Global Optimization 69, no. 1 (October 28, 2016): 157–82. http://dx.doi.org/10.1007/s10898-016-0473-x.

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12

Fischetti, Matteo, and Andrea Lodi. "Repairing MIP infeasibility through local branching." Computers & Operations Research 35, no. 5 (May 2008): 1436–45. http://dx.doi.org/10.1016/j.cor.2006.08.004.

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13

Atlihan, Mustafa K., and Linus Schrage. "Generalized filtering algorithms for infeasibility analysis." Computers & Operations Research 35, no. 5 (May 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, no. 5 (May 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, no. 1 (February 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, and QIYAN WANG. "INFEASIBILITY OF QUANTUM CRYPTOGRAPHY WITHOUT EAVESDROPPING CHECK." International Journal of Modern Physics B 25, no. 08 (March 30, 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, no. 4 (October 28, 2013): 471–89. http://dx.doi.org/10.1108/jaoc-08-2010-0038.

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18

Xu, Zheng, Jian Yang, and Nengjin Sheng. "Infeasibility Analysis of Half-Wavelength Transmission Systems." Energies 11, no. 7 (July 8, 2018): 1790. http://dx.doi.org/10.3390/en11071790.

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19

Caraffini, Fabio, Anna V. Kononova, and David Corne. "Infeasibility and structural bias in differential evolution." Information Sciences 496 (September 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, and Salvatore J. Stolfo. "On the infeasibility of modeling polymorphic shellcode." Machine Learning 81, no. 2 (October 29, 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, no. 3 (September 1998): 280–91. http://dx.doi.org/10.1177/01466216980223008.

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22

Applegate, David L., William Cook, and S. Thomas McCormick. "Integral infeasibility and testing total dual integrality." Operations Research Letters 10, no. 1 (February 1991): 37–41. http://dx.doi.org/10.1016/0167-6377(91)90084-3.

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23

Lourenço, Bruno F., Masakazu Muramatsu, and Takashi Tsuchiya. "Weak infeasibility in second order cone programming." Optimization Letters 10, no. 8 (December 24, 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, and Shing-Chih Yang. "Revised DEMATEL: Resolving the Infeasibility of DEMATEL." Applied Mathematical Modelling 37, no. 10-11 (June 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, and LI Sikun. "Extracting minimal unsatisfiable subformulas in satisfiability modulo theories." Computer Science and Information Systems 8, no. 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, and Zheng-Qun Cai. "Super-Efficiency Infeasibility in the Presence of Nonradial Measurement." Mathematical Problems in Engineering 2020 (September 19, 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, no. 3 (June 1998): 633–43. http://dx.doi.org/10.1016/s0377-2217(97)00168-9.

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28

Hsiao-Fan Wang and Hsueh-Ling Liao. "Fuzzy resolution on the infeasibility of variational inequality." European Journal of Operational Research 106, no. 1 (April 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, and Bjarne A. Foss. "Stabilizing Linear MPC with Efficient Prioritized Infeasibility Handling." IFAC Proceedings Volumes 33, no. 10 (June 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, and J. Zhu. "A modified super-efficiency DEA model for infeasibility." Journal of the Operational Research Society 60, no. 2 (February 2009): 276–81. http://dx.doi.org/10.1057/palgrave.jors.2602544.

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31

Denning, Karen C., Stephen P. Ferris, and Robert M. Lawless. "Serial bankruptcy: plan infeasibility or just bad luck?" Applied Economics Letters 8, no. 2 (February 2001): 105–9. http://dx.doi.org/10.1080/13504850150204156.

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32

Byrd, Richard H., Frank E. Curtis, and Jorge Nocedal. "Infeasibility Detection and SQP Methods for Nonlinear Optimization." SIAM Journal on Optimization 20, no. 5 (January 2010): 2281–99. http://dx.doi.org/10.1137/080738222.

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33

Bielschowsky, Roberto H., and Francisco A. M. Gomes. "Dynamic Control of Infeasibility in Equality Constrained Optimization." SIAM Journal on Optimization 19, no. 3 (January 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, no. 1 (June 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, no. 1 (January 1994): 70–89. http://dx.doi.org/10.1061/(asce)0733-9496(1994)120:1(70).

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36

Seiford, Lawrence M., and Joe Zhu. "Infeasibility Of Super-Efficiency Data Envelopment Analysis Models." INFOR: Information Systems and Operational Research 37, no. 2 (May 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, no. 1 (March 1996): 155–76. http://dx.doi.org/10.1007/bf02284629.

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38

Lee, Hsuan-Shih, Ching-Wu Chu, and Joe Zhu. "Super-efficiency DEA in the presence of infeasibility." European Journal of Operational Research 212, no. 1 (July 2011): 141–47. http://dx.doi.org/10.1016/j.ejor.2011.01.022.

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39

Lee, Hsuan-Shih, and Joe Zhu. "Super-efficiency infeasibility and zero data in DEA." European Journal of Operational Research 216, no. 2 (January 2012): 429–33. http://dx.doi.org/10.1016/j.ejor.2011.07.050.

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40

Takemura, Akimichi, and Ruriko Yoshida. "A generalization of the integer linear infeasibility problem." Discrete Optimization 5, no. 1 (February 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, and C. A. K. Lovell. "Avoiding infeasibility in DEA models with weight restrictions." European Journal of Operational Research 181, no. 2 (September 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, and Bijan Sayyar-Rodsari. "Infeasibility resolution for multi-purpose batch process scheduling." Computers & Chemical Engineering 116 (August 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, and Fangqing Wei. "Performance Ranking Method Based on Superefficiency with Directional Distance Function in DEA." Mathematical Problems in Engineering 2020 (February 18, 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, and Mr Amit Bhanot. "Traveling Salesman Problem: A Case Study." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 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, no. 2 (February 5, 2019): 195–215. http://dx.doi.org/10.24869/psihei.2018.195.

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46

Wagler, Annegret K., and Jan-Thierry Wegener. "Preprocessing for Network Reconstruction: Feasibility Test and Handling Infeasibility." Fundamenta Informaticae 135, no. 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, and Susan Margulies. "Computing infeasibility certificates for combinatorial problems through Hilbert’s Nullstellensatz." Journal of Symbolic Computation 46, no. 11 (November 2011): 1260–83. http://dx.doi.org/10.1016/j.jsc.2011.08.007.

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48

Singh, Hemant Kumar, Tapabrata Ray, and Ruhul Sarker. "Optimum Oil Production Planning Using Infeasibility Driven Evolutionary Algorithm." Evolutionary Computation 21, no. 1 (March 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, and Nikolaos V. Sahinidis. "Deletion Presolve for Accelerating Infeasibility Diagnosis in Optimization Models." INFORMS Journal on Computing 29, no. 4 (November 2017): 754–66. http://dx.doi.org/10.1287/ijoc.2017.0761.

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50

Gohari, Amin, and Venkat Anantharam. "Infeasibility Proof and Information State in Network Information Theory." IEEE Transactions on Information Theory 60, no. 10 (October 2014): 5992–6004. http://dx.doi.org/10.1109/tit.2014.2347301.

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