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Journal articles on the topic 'Problem Solver'

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Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2023

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1

Gogodze, Joseph. "Ranking Methods for Multicriteria Decision-Making: Application to Benchmarking of Solvers and Problems." Scientific Programming 2021 (July 11, 2021): 1–14. http://dx.doi.org/10.1155/2021/5513860.

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Evaluating the performance assessments of solvers (e.g., for computation programs), known as the solver benchmarking problem, has become a topic of intense study, and various approaches have been discussed in the literature. Such a variety of approaches exist because a benchmark problem is essentially a multicriteria problem. In particular, the appropriate multicriteria decision-making problem can correspond naturally to each benchmark problem and vice versa. In this study, to solve the solver benchmarking problem, we apply the ranking-theory method recently proposed for solving multicriteria decision-making problems. The benchmarking problem of differential evolution algorithms was considered for a case study to illustrate the ability of the proposed method. This problem was solved using ranking methods from different areas of origin. The comparisons revealed that the proposed method is competitive and can be successfully used to solve benchmarking problems and obtain relevant engineering decisions. This study can help practitioners and researchers use multicriteria decision-making approaches for benchmarking problems in different areas, particularly software benchmarking.

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2

Joseph, Ron. "Painting problem solver." Metal Finishing 106, no. 9 (September 2008): 56–57. http://dx.doi.org/10.1016/s0026-0576(08)80289-9.

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3

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 1 (January 2009): 47. http://dx.doi.org/10.1016/s0026-0576(09)80010-x.

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4

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 3 (March 2009): 44–45. http://dx.doi.org/10.1016/s0026-0576(09)80051-2.

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5

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 4 (January 2009): 62. http://dx.doi.org/10.1016/s0026-0576(09)80078-0.

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6

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 7-8 (July 2009): 47. http://dx.doi.org/10.1016/s0026-0576(09)80206-7.

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7

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 9 (September 2009): 51–52. http://dx.doi.org/10.1016/s0026-0576(09)80234-1.

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8

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 10 (October 2009): 45. http://dx.doi.org/10.1016/s0026-0576(09)80256-0.

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9

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 6 (June 2009): 60–62. http://dx.doi.org/10.1016/s0026-0576(09)80298-5.

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10

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 11 (November 2009): 34. http://dx.doi.org/10.1016/s0026-0576(09)80375-9.

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11

Joseph, Ron. "Painting problem solver." Metal Finishing 107, no. 12 (December 2009): 45. http://dx.doi.org/10.1016/s0026-0576(09)80426-1.

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12

Joseph, Ron. "Painting problem solver." Metal Finishing 108, no. 1 (January 2010): 41. http://dx.doi.org/10.1016/s0026-0576(10)80006-6.

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13

Joseph, Ron. "Painting problem solver." Metal Finishing 108, no. 2 (February 2010): 38–39. http://dx.doi.org/10.1016/s0026-0576(10)80040-6.

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14

Joseph, Ron. "Painting problem solver." Metal Finishing 108, no. 3 (March 2010): 42–44. http://dx.doi.org/10.1016/s0026-0576(10)80079-0.

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15

Joseph, Ron. "Painting problem solver." Metal Finishing 108, no. 5 (May 2010): 19. http://dx.doi.org/10.1016/s0026-0576(10)80101-1.

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16

Joseph, Ron. "Painting problem solver." Metal Finishing 108, no. 6 (June 2010): 63–64. http://dx.doi.org/10.1016/s0026-0576(10)80136-9.

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17

Joseph, Ron. "Painting problem solver." Metal Finishing 108, no. 9 (September 2010): 23–24. http://dx.doi.org/10.1016/s0026-0576(10)80184-9.

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18

Cattani, Tiziano. "Piston Problem Solver." Industrial Vehicle Technology International 28, no. 1 (February 2020): 78. http://dx.doi.org/10.12968/s1471-115x(23)70486-8.

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19

Laue, Sören, Matthias Mitterreiter, and Joachim Giesen. "GENO – Optimization for Classical Machine Learning Made Fast and Easy." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13620–21. http://dx.doi.org/10.1609/aaai.v34i09.7097.

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Most problems from classical machine learning can be cast as an optimization problem. We introduce GENO (GENeric Optimization), a framework that lets the user specify a constrained or unconstrained optimization problem in an easy-to-read modeling language. GENO then generates a solver, i.e., Python code, that can solve this class of optimization problems. The generated solver is usually as fast as hand-written, problem-specific, and well-engineered solvers. Often the solvers generated by GENO are faster by a large margin compared to recently developed solvers that are tailored to a specific problem class.An online interface to our framework can be found at http://www.geno-project.org.

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20

Buschman, Larry. "Becoming a Problem Solver." Teaching Children Mathematics 9, no. 2 (October 2002): 98–103. http://dx.doi.org/10.5951/tcm.9.2.0098.

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Over the past ten years, I have attempted to identify the characteristics of young children as they grow and mature as problem solvers by conducting an action research project in a first- through third-grade classroom. Teaching in a multiage classroom gave me the opportunity to observe children over a period of three years and to document their progress using various types of assessment. I observed children while they were engaged in the act of solving problems and sharing solutions with others. I interviewed children as they solved problems and I scored their written work using a scoring guide, or rubric.

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21

Wark, David. "The supreme problem solver." Nature 445, no. 7124 (January 2007): 149–50. http://dx.doi.org/10.1038/445149a.

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22

Tao, Terence Chi-Shen. "Jean Bourgain, problem solver." Proceedings of the National Academy of Sciences 116, no. 28 (June 17, 2019): 13717–18. http://dx.doi.org/10.1073/pnas.1901965116.

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23

Fritz, W. B. "ENIAC-a problem solver." IEEE Annals of the History of Computing 16, no. 1 (1994): 25–45. http://dx.doi.org/10.1109/85.251853.

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24

Davies, S. "Problem solver [factory automation]." Computing and Control Engineering 17, no. 2 (April 1, 2006): 22–25. http://dx.doi.org/10.1049/cce:20060202.

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25

Schmidhuber, Jürgen. "Optimal Ordered Problem Solver." Machine Learning 54, no. 3 (March 2004): 211–54. http://dx.doi.org/10.1023/b:mach.0000015880.99707.b2.

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26

Tattersall, Michelle. "The Communication Problem Solver." AORN Journal 92, no. 1 (July 2010): 123–25. http://dx.doi.org/10.1016/j.aorn.2010.05.002.

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27

Gajewski, Ryszard Robert. "Evolutionary Spreadsheet Solver in Optimal Engineering Design." MATEC Web of Conferences 196 (2018): 01047. http://dx.doi.org/10.1051/matecconf/201819601047.

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Spreadsheet solver proved to be an excellent tool to solve operational research problems modelled as linear programming problems. Majority of engineering design problems are nonlinear in nature. The paper presents ability of spreadsheet solver to solve such problems as: four bar statically determinate truss, compound gear train problem and sequence determination problem by means of evolutionary engine.

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28

Sano, Norihide, and Ryoichi Takahashi. "Modified and Fuzzified General Problem Solver for "Monkey and Banana" Problem : Strategy of General Problem Solver." JSME international journal. Ser. C, Dynamics, control, robotics, design and manufacturing 37, no. 1 (1994): 130–37. http://dx.doi.org/10.1299/jsmec1993.37.130.

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29

Pinheiro, Placido Rogerio, Andre Luis Vasconcelos Coelho, Alexei Barbosa Aguiar, and Alvaro de Menezes Sobreira Neto. "Towards Aid by Generate and Solve Methodology: Application in the Problem of Coverage and Connectivity in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 10 (October 1, 2012): 790459. http://dx.doi.org/10.1155/2012/790459.

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The integrative collaboration of genetic algorithms and integer linear programming as specified by the Generate and Solve methodology tries to merge their strong points and has offered significant results when applied to wireless sensor networks domains. The Generate and Solve (GS) methodology is a hybrid approach that combines a metaheuristics component with an exact solver. GS has been recently introduced into the literature in order to solve the problem of dynamic coverage and connectivity in wireless sensor networks, showing promising results. The GS framework includes a metaheuristics engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver.

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30

Sonobe, Tomohiro. "An Experimental Survey of Extended Resolution Effects for SAT Solvers on the Pigeonhole Principle." Algorithms 15, no. 12 (December 16, 2022): 479. http://dx.doi.org/10.3390/a15120479.

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It has been proven that extended resolution (ER) has more powerful reasoning than general resolution for the pigeonhole principle in Cook’s paper. This fact indicates the possibility that a solver based on extended resolution can exceed Boolean satisfiability problem solvers (SAT solvers for short) based on general resolution. However, few studies have provided practical evidence of this assumption. This paper explores how extended resolution can improve SAT solvers by using the pigeonhole principle, which was the first problem solved by ER in polynomial steps. In fact, although Cook’s paper introduced how to add auxiliary variables, there is no evidence that these variables are really useful for practical solvers. We try to answer the question: If the SAT solver can add appropriate auxiliary variables as proposed in Cook’s paper, can the solver enhance its performance by utilizing these variables? Experimental results show that if the solver properly prioritizes the extended variables in the search, the solver can end the search in a shorter time.

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31

Qiu, Changkai, Changchun Yin, Yunhe Liu, Xiuyan Ren, Hui Chen, and Tingjie Yan. "Solution of large-scale 3D controlled-source electromagnetic modeling problem using efficient iterative solvers." GEOPHYSICS 86, no. 4 (June 30, 2021): E283—E296. http://dx.doi.org/10.1190/geo2020-0461.1.

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With geophysical surveys evolving from traditional 2D to 3D models, the large volume of data adds challenges to inversion, especially when aiming to resolve complex 3D structures. An iterative forward solver for the controlled-source electromagnetic (CSEM) method requires less memory than that for a direct solver; however, it is not easy to iteratively solve an ill-conditioned linear system of equations arising from finite-element discretization of Maxwell’s equations. To solve this problem, we have developed efficient and robust iterative solvers for frequency- and time-domain CSEM modeling problems. For the frequency-domain problem, we first transform the linear system into its equivalent real-number format, and then introduce an optimal block-diagonal preconditioner. Because the condition number of the preconditioned linear equation system has an upper bound of [Formula: see text], we can achieve fast solution convergence when applying a flexible generalized minimum residual solver. Applying the block preconditioner further results in solving two smaller linear systems with the same coefficient matrix. For the time-domain problem, we first discretize the partial differential equation for the electric field in time using an unconditionally stable backward Euler scheme. We then solve the resulting linear equation system iteratively at each time step. After the spatial discretization in the frequency domain, or space-time discretization in the time domain, we exploit the conjugate-gradient solver with auxiliary-space preconditioners derived from the Hiptmair-Xu decomposition to solve these real linear systems. Finally, we check the efficiency and effectiveness of our iterative methods by simulating complex CSEM models. The most significant advantage of our approach is that the iterative solvers we adopt have almost the same accuracy and robustness as direct solvers but require much less memory, rendering them more suitable for large-scale 3D CSEM forward modeling and inversion.

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32

Fichte, Johannes K., Markus Hecher, Michael Morak, and Stefan Woltran. "DynASP2.5: Dynamic Programming on Tree Decompositions in Action." Algorithms 14, no. 3 (March 2, 2021): 81. http://dx.doi.org/10.3390/a14030081.

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Efficient exact parameterized algorithms are an active research area. Such algorithms exhibit a broad interest in the theoretical community. In the last few years, implementations for computing various parameters (parameter detection) have been established in parameterized challenges, such as treewidth, treedepth, hypertree width, feedback vertex set, or vertex cover. In theory, instances, for which the considered parameter is small, can be solved fast (problem evaluation), i.e., the runtime is bounded exponential in the parameter. While such favorable theoretical guarantees exists, it is often unclear whether one can successfully implement these algorithms under practical considerations. In other words, can we design and construct implementations of parameterized algorithms such that they perform similar or even better than well-established problem solvers on instances where the parameter is small. Indeed, we can build an implementation that performs well under the theoretical assumptions. However, it could also well be that an existing solver implicitly takes advantage of a structure, which is often claimed for solvers that build on Sat-solving. In this paper, we consider finding one solution to instances of answer set programming (ASP), which is a logic-based declarative modeling and solving framework. Solutions for ASP instances are so-called answer sets. Interestingly, the problem of deciding whether an instance has an answer set is already located on the second level of the polynomial hierarchy. An ASP solver that employs treewidth as parameter and runs dynamic programming on tree decompositions is DynASP2. Empirical experiments show that this solver is fast on instances of small treewidth and can outperform modern ASP when one counts answer sets. It remains open, whether one can improve the solver such that it also finds one answer set fast and shows competitive behavior to modern ASP solvers on instances of low treewidth. Unfortunately, theoretical models of modern ASP solvers already indicate that these solvers can solve instances of low treewidth fast, since they are based on Sat-solving algorithms. In this paper, we improve DynASP2 and construct the solver DynASP2.5, which uses a different approach. The new solver shows competitive behavior to state-of-the-art ASP solvers even for finding just one solution. We present empirical experiments where one can see that our new implementation solves ASP instances, which encode the Steiner tree problem on graphs with low treewidth, fast. Our implementation is based on a novel approach that we call multi-pass dynamic programming (M-DPSINC). In the paper, we describe the underlying concepts of our implementation (DynASP2.5) and we argue why the techniques still yield correct algorithms.

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33

Ballew, Hunter. "Sherlock Holmes, Master Problem Solver." Mathematics Teacher 87, no. 8 (November 1994): 596–601. http://dx.doi.org/10.5951/mt.87.8.0596.

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34

Rigelman, Nicole R. "Becoming a Mathematical Problem Solver." Mathematics Teaching in the Middle School 18, no. 7 (March 2013): 416–23. http://dx.doi.org/10.5951/mathteacmiddscho.18.7.0416.

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35

Kulinski, Alexa R. "Awakening the Creative Problem Solver." Art Education 71, no. 5 (August 20, 2018): 42–47. http://dx.doi.org/10.1080/00043125.2018.1482165.

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36

Colenutt, Christina E., and Ronald E. McCarville. "The Client as Problem Solver:." Journal of Hospitality & Leisure Marketing 2, no. 3 (April 4, 1995): 23–35. http://dx.doi.org/10.1300/j150v02n03_03.

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37

Ubhiriyani, Mr Lokesh. "INCLUSIVE EDUCATION: A PROBLEM SOLVER." BSSS Journal of Education 11, no. 1 (June 30, 2022): 127–36. http://dx.doi.org/10.51767/je1110.

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Education is a powerful tool to bring a change in society and nation. Inclusive education means providing opportunity to an individual to get education with ability and disability. Inclusive education plays a vital role in bridging a gap between normal and disabled child with respect and acceptance. According to new education policy 2020, every child has the right to be educated in 21st century so there is a high demand and need to implement inclusive education with proper infrastructure and quality education such as training of teachers to deal with different disabilities prevails in society, technology advancement, good communication skills and mastery of particular subject. According to the right of person with disabilities (RPWD) “every disabled child has a right to be educated with normal child in the same classroom with some changes in infrastructure such as braille technology in Bihar for visually disabled child” which is a great initiative taken by Government of Bihar and RPWD in past and present years. It is duty of every school and college to adopt the inclusive classroom to enhance the quality of education which will result in cooperative learning between normal and special child.

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38

Nucci, Larry P. "The Child as Problem Solver." Contemporary Psychology: A Journal of Reviews 41, no. 11 (November 1996): 1110–11. http://dx.doi.org/10.1037/003206.

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39

Glazer, Jacob, and Ariel Rubinstein. "Coordinating with a “Problem Solver”." Management Science 65, no. 6 (June 2019): 2813–19. http://dx.doi.org/10.1287/mnsc.2018.3078.

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40

Nambi, Parthasarathy. "Eureka: A chemistry problem solver." Journal of Chemical Education 66, no. 6 (June 1989): A163. http://dx.doi.org/10.1021/ed066pa163.1.

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41

Bieszczad, Andrzej, and Bernard Pagurek. "Neurosolver: Neuromorphic general problem solver." Information Sciences 105, no. 1-4 (March 1998): 239–77. http://dx.doi.org/10.1016/s0020-0255(97)10027-5.

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42

Cabrol, D., C. Cachet, and R. Cornelius. "A heuristic problem solver: George." Computers & Education 10, no. 1 (January 1986): 81–87. http://dx.doi.org/10.1016/0360-1315(86)90055-2.

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43

Orispää, Mikko, and Markku Lehtinen. "Fortran linear inverse problem solver." Inverse Problems & Imaging 4, no. 3 (2010): 485–503. http://dx.doi.org/10.3934/ipi.2010.4.485.

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44

Murata, Tsuyoshi, Hiroki Kato, Masamichi Shimura, and Masayuki Numao. "DIPS: Diagrammatic arithmetic problem solver." Systems and Computers in Japan 33, no. 6 (April 24, 2002): 112–20. http://dx.doi.org/10.1002/scj.1138.

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45

Yu, Wen-der, Jyh-bin Yang, Judy C. R. Tseng, Shen-jung Liu, and Ji-wei Wu. "Proactive problem-solver for construction." Automation in Construction 19, no. 6 (October 2010): 808–16. http://dx.doi.org/10.1016/j.autcon.2010.05.003.

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46

Duan, C. J., Jiayu Hu, and Stephen C. Garrott. "Using Excel Solver to solve braydon farms' truck routing problem: A case study." South Asian Journal of Management Sciences 10, no. 1 (March 2016): 38–47. http://dx.doi.org/10.21621/sajms.2016101.04.

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47

L. Kaizer, Wesley, André G. Pereira, and Marcus Ritt. "Sequencing Operator Counts with State-Space Search." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 166–74. http://dx.doi.org/10.1609/icaps.v30i1.6658.

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A search algorithm with an admissible heuristic function is the most common approach to optimally solve classical planning tasks. Recently Daviesetal.(2015) introduced the solver OpSeq using Logic-Based Benders Decomposition to solve planning tasks optimally. In this approach, the master problem is an integer program derived from the operator-counting framework that generates operator counts, i.e., an assignment of integer counts for each task operator. Then, the operator counts sequencing subproblem verifies if a plan satisfying these operator counts exists, or generates a necessary violated constraint to strengthen the master problem. In OpSeq the subproblem is solved by a SAT solver. In this paper we show that operator counts sequencing can be better solved by state-space search. We introduce OpSearch, an A∗-based algorithm to solve the operator counts sequencing subproblem: it either finds an optimal plan, or uses the frontier of the search to derive a violated constraint. We show that using a standard search framework has two advantages: i) search scales better than a SAT-based approach for solving the operator counts sequencing, ii) explicit information in the search frontier can be used to derive stronger constraints. We present results on the IPC-2011 benchmarks showing that this approach solves more planning tasks, using less memory. On tasks solved by both methods OpSearch usually requires to solve fewer operator counts sequencing problems than OpSeq, evidencing the stronger constraints generated by OpSearch.

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48

Spence, Mark T. "Problem–Problem Solver Characteristics Affectingthe Calibration of Judgments." Organizational Behavior and Human Decision Processes 67, no. 3 (September 1996): 271–79. http://dx.doi.org/10.1006/obhd.1996.0079.

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49

SANO, Norihide, and Ryoichi TAKAHASHI. "Modified and Fuzzified General Problem Solver for "Monkey and Banana" Problem. 2nd Report, Strategy on General Problem Solver." Transactions of the Japan Society of Mechanical Engineers Series C 57, no. 543 (1991): 3599–605. http://dx.doi.org/10.1299/kikaic.57.3599.

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50

Han, Choongyong, John Wallis, Pallav Sarma, Gary Li, Mark L. Schrader, and Wen Chen. "Adaptation of the CPR Preconditioner for Efficient Solution of the Adjoint Equation." SPE Journal 18, no. 02 (January 14, 2013): 207–13. http://dx.doi.org/10.2118/141300-pa.

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Summary It is well known that the adjoint approach is the most efficient approach for gradient calculation, and it can be used with gradient-based optimization techniques to solve various optimization problems, such as the production-optimization problem and the history-matching problem. The adjoint equation to be solved in the approach is a linear equation formed with the “transpose” of the Jacobian matrix from a fully implicit reservoir simulator. For a large and/or complex reservoir model, generalized preconditioners often prove impractical for solving the adjoint equation. Preconditioners specialized for reservoir simulation, such as constrained pressure residual (CPR), exploit properties of the Jacobian matrix to accelerate convergence, so they cannot be applied directly to the adjoint equation. To overcome this challenge, we have developed a new two-stage preconditioner for efficient solution of the adjoint equation by adaptation of the CPR preconditioner (named CPRA: CPR preconditioner for adjoint equation). The CPRA preconditioner has been coupled with an algebraic multigrid (AMG) linear solver and implemented in Chevron's extended applications reservoir simulator (CHEARS®). The AMG solver is well known for its outstanding capability to solve the pressure equation of complex reservoir models; solving the linear system with the “transpose” of the pressure matrix is one of the two stages of construction of the CPRA preconditioner. Through test cases, we have confirmed that the CPRA/AMG solver with generalized minimal residual (GMRES) acceleration solves the adjoint equation very efficiently with a reasonable number of linear-solver iterations. Adjoint simulations to calculate the gradients with the CPRA/AMG solver take approximately the same amount of time (at most) as do the corresponding CPR/AMG forward simulations. Accuracy of the solutions has also been confirmed by verifying the gradients against solutions with a direct solver. A production-optimization case study for a real field using the CPRA/AMG solver has further validated its accuracy, efficiency, and the capability to perform long-term optimization for large, complex reservoir models at low computational cost.

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