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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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 witho

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17

Sawanobori, Chie. "Influence of fundraising infeasibility on accounting changes." Journal of Accounting & Organizational Change 9, no. 4 (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 (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 (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 (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 (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 (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 (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 bre

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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 alloca

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27

Obuchowska, Wieslawa T. "Infeasibility analysis for systems of quadratic convex inequalities." European Journal of Operational Research 107, no. 3 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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

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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 (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 (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 (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 (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 ex

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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 (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 (2014): 5992–6004. http://dx.doi.org/10.1109/tit.2014.2347301.

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