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Journal articles on the topic 'Methods of problem solving'

Author: Grafiati

Published: 28 June 2021

Last updated: 7 February 2022

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1

BENJAMINS, V. RICHARD, and DIETER FENSEL. "Editorial: problem-solving methods." International Journal of Human-Computer Studies 49, no. 4 (October 1998): 305–13. http://dx.doi.org/10.1006/ijhc.1998.0208.

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FENSEL, DIETER, and ARNO SCH Ö. "Inverse verification of problem-solving methods." International Journal of Human-Computer Studies 49, no. 4 (October 1998): 339–61. http://dx.doi.org/10.1006/ijhc.1998.0210.

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3

Fensel, D., and E. Motta. "Structured development of problem solving methods." IEEE Transactions on Knowledge and Data Engineering 13, no. 6 (2001): 913–32. http://dx.doi.org/10.1109/69.971187.

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4

Antonietti, Alessandro, Sabrina Ignazi, and Patrizia Perego. "Metacognitive knowledge about problem-solving methods." British Journal of Educational Psychology 70, no. 1 (March 2000): 1–16. http://dx.doi.org/10.1348/000709900157921.

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Golichev, Iosif Iosifovich, Timur Rafailevich Sharipov, and Natal'ya Iosifovna Luchnikova. "Gradient methods for solving Stokes problem." Ufimskii Matematicheskii Zhurnal 8, no. 2 (2016): 22–38. http://dx.doi.org/10.13108/2016-8-2-22.

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6

Vabishchevich, Petr N. "Iterative Methods for Solving Convection-diffusion Problem." Computational Methods in Applied Mathematics 2, no. 4 (2002): 410–44. http://dx.doi.org/10.2478/cmam-2002-0023.

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AbstractTo obtain an approximate solution of the steady-state convectiondiffusion problem, it is necessary to solve the corresponding system of linear algebraic equations. The basic peculiarity of these LA systems is connected with the fact that they have non-symmetric matrices. We discuss the questions of approximate solution of 2D convection-diffusion problems on the basis of two- and three-level iterative methods. The general theory of iterative methods of solving grid equations is used to present the material of the paper. The basic problems of constructing grid approximations for steady-state convection-diffusion problems are considered. We start with the consideration of the Dirichlet problem for the differential equation with a convective term in the divergent, nondivergent, and skew-symmetric forms. Next, the corresponding grid problems are constructed. And, finally, iterative methods are used to solve approximately the above grid problems. Primary consideration is given to the study of the dependence of the number of iteration on the Peclet number, which is the ratio of the convective transport to the diffusive one.

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Dr. G. Geetharamani, Dr G. Geetharamani, and C. Sharmila Devi. "An Innovative Method for Solving Fuzzy Transportation Problem." Indian Journal of Applied Research 4, no. 5 (October 1, 2011): 399–402. http://dx.doi.org/10.15373/2249555x/may2014/124.

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Pekár, Juraj, Ivan Brezina, Jaroslav Kultan, Iryna Ushakova, and Oleksandr Dorokhov. "Computer tools for solving the traveling salesman problem." Development Management 18, no. 1 (June 30, 2020): 25–39. http://dx.doi.org/10.21511/dm.18(1).2020.03.

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The task of the traveling salesman, which is to find the shortest or least costly circular route, is one of the most common optimization problems that need to be solved in various fields of practice. The article analyzes and demonstrates various methods for solving this problem using a specific example: heuristic (the nearest neighbor method, the most profitable neighbor method), metaheuristic (evolutionary algorithm), methods of mathematical programming. In addition to classic exact methods (which are difficult to use for large-scale tasks based on existing software) and heuristic methods, the article suggests using the innovative features of the commercially available MS Excel software using a meta-heuristic base. To find the optimal solution using exact methods, the Excel (Solver) software package was used, as well as the specialized GAMS software package. Comparison of different approaches to solving the traveling salesman problem using a practical example showed that the use of traditional heuristic approaches (the nearest neighbor method or the most profitable neighbor method) is not difficult from a computational point of view, but does not provide solutions that would be acceptable in modern conditions. The use of MS Excel for solving the problem using the methods of mathematical programming and metaheuristics enabled us to obtain an optimal solution, which led to the conclusion that modern tools are an appropriate addition to solving the traveling salesman problem while maintaining the quality of the solution.

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Yamashiro, Seiji. "Problem solving～Reconsider methods and tools for problem solving at the point of care～." Nihon Naika Gakkai Zasshi 106, no. 12 (December 10, 2017): 2519–22. http://dx.doi.org/10.2169/naika.106.2519.

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10

Au, Wing K., and John P. Leung. "Problem Solving, Instructional Methods and Logo Programming." Journal of Educational Computing Research 7, no. 4 (November 1991): 455–67. http://dx.doi.org/10.2190/k88q-rwv1-avpu-3dtk.

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11

LUO, J., and G. KNOBLICH. "Studying insight problem solving with neuroscientific methods☆." Methods 42, no. 1 (May 2007): 77–86. http://dx.doi.org/10.1016/j.ymeth.2006.12.005.

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12

Brown, David C. "Problem Solving Methods: Past, Present, and Future." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23, no. 4 (October 14, 2009): 327–29. http://dx.doi.org/10.1017/s0890060409990023.

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13

G, Jose Manuel, and Oscar Corcho. "Problem-Solving Methods for Understanding Process Executions." Computing in Science & Engineering 10, no. 3 (May 2008): 47–52. http://dx.doi.org/10.1109/mcse.2008.78.

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14

Gupta, Madhu. "Educator's Corner: Learning methods of problem solving." IEEE Microwave Magazine 9, no. 5 (October 2008): 134–65. http://dx.doi.org/10.1109/mmm.2008.927643.

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15

Stefanović, Nebojša. "Innovative Problem Solving Methods in Education Field." Education Journal 2, no. 2 (2013): 27. http://dx.doi.org/10.11648/j.edu.20130202.12.

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16

Mortici, Cristinel. "Approximation Methods for Solving the Cauchy Problem." Czechoslovak Mathematical Journal 55, no. 3 (September 2005): 709–18. http://dx.doi.org/10.1007/s10587-005-0058-1.

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17

GOMEZ, MARIO, ENRIC PLAZA, and CHEMA ABASOLO. "PROBLEM-SOLVING METHODS AND COOPERATIVE INFORMATION AGENTS." International Journal of Cooperative Information Systems 11, no. 03n04 (September 2002): 329–54. http://dx.doi.org/10.1142/s0218843002000625.

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Cooperative Information Agents and modern information systems in general have to access large amounts of information distributed across multiple heterogeneous sources. A great challenge of such systems is to evolve by adding new information sources or adapting the existing components for different domain knowledge. We propose the [Formula: see text] framework as a methodology to build Information Agents by reusing a library of problem-solving components that are defined in a domain-independent manner. Moreover, the [Formula: see text] language is used as an Agent Capability Description Language (ACDL) suitable to configure an agent-based application. From this approach, a new application is built by linking the components of the library with a particular domain and a collection of heterogeneous information sources. Adaptability and dynamic configuration of such a system is achieved by reasoning about the [Formula: see text] specification of the agent capabilities. Independence of the domain and semantic interoperability are achieved by using ontologies and bridges (mappings between ontologies), while independence from the information sources is based again in the use of ontologies to overcome semantic heterogeneity and wrappers to achieve syntactic interoperability.

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18

Eriksson, Henrik, Yuval Shahar, Samson W. Tu, Angel R. Puerta, and Mark A. Musen. "Task modeling with reusable problem-solving methods." Artificial Intelligence 79, no. 2 (December 1995): 293–326. http://dx.doi.org/10.1016/0004-3702(94)00040-9.

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19

Hori, Masahiro, Yuichi Nakamura, and Toshiyuki Hama. "Configuring problem-solving methods: a CAKE perspective." Knowledge Acquisition 6, no. 4 (December 1994): 461–87. http://dx.doi.org/10.1006/knac.1994.1021.

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20

Mozgovoy, A. V. "Methods of constructing basis in solving inverse problems." Functional materials 21, no. 4 (December 30, 2014): 457–62. http://dx.doi.org/10.15407/fm21.04.457.

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21

Wiener, Joseph, and Will Watkins. "Problem Solving Also Raises Questions." Mathematics Teacher 81, no. 9 (December 1988): 729–32. http://dx.doi.org/10.5951/mt.81.9.0729.

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Students sometimes confuse problem solving with getting an answer. Indeed, correctly stating and justifying an answer to a well-posed problem is an elementary example of problem solving. However, the student who poses problems and identifies increasingly general methods for solving those problems is gaining problem-solving maturity.

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22

Baboş, Alina. "Statistical Methods for Solving Transportation Problems." International conference KNOWLEDGE-BASED ORGANIZATION 25, no. 2 (June 1, 2019): 10–13. http://dx.doi.org/10.2478/kbo-2019-0049.

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Abstract Transportation problem is one of the models of Linear Programming problem. It deals with the situation in which a commodity from several sources is shipped to different destinations with the main objective to minimize the total shipping cost. There are three well-known methods namely, North West Corner Method Least Cost Method, Vogel’s Approximation Method to find the initial basic feasible solution of a transportation problem. In this paper, we present some statistical methods for finding the initial basic feasible solution. We use three statistical tools: arithmetic and harmonic mean and median. We present numerical examples, and we compare these results with other classical methods.

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23

Veal, William. "Chemical Reaction Problem Solving." Hoosier Science Teacher 40, no. 1 (February 2, 2017): 16–21. http://dx.doi.org/10.14434/thst.v40i1.23275.

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24

Thilmany, Jean. "Probabilistic Problem Solving." Mechanical Engineering 124, no. 01 (January 1, 2002): 53–55. http://dx.doi.org/10.1115/1.2002-jan-4.

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This article reviews predictive technologies based on a probabilistic method of problem solving. These technologies are gaining a steady foothold as a method of finding answers to engineering and other types of problems. According to the developer of one such technology, these computer programs use mathematical models to predict the probability that something will or won’t happen a particular way in the future. The tools can be used for design, sensitivity analysis, mathematical modeling of complex processes, uncertainty analysis, competitive analysis, and process optimization among other things. The predictive technology from Unipass has been used by the research center to design gas turbines, helicopters, and elevators. The probabilistic method and the newer predictive technologies that use it have some ardent backers. For instance, the probabilistic methods committee of the Society of Automotive Engineers states its mission as: to enable and facilitate rapid deployment of probabilistic technology to enhance the competitiveness of our industries by better, faster, greener, smarter, affordable, and reliable product development.

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25

Qu, Biao, and Jing Zhao. "Methods for Solving Generalized Nash Equilibrium." Journal of Applied Mathematics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/762165.

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The generalized Nash equilibrium problem (GNEP) is an extension of the standard Nash equilibrium problem (NEP), in which each player's strategy set may depend on the rival player's strategies. In this paper, we present two descent type methods. The algorithms are based on a reformulation of the generalized Nash equilibrium using Nikaido-Isoda function as unconstrained optimization. We prove that our algorithms are globally convergent and the convergence analysis is not based on conditions guaranteeing that every stationary point of the optimization problem is a solution of the GNEP.

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26

Safina, G. L. "Solving of the filtration problem by numerical methods." Вестник гражданских инженеров 16, no. 4 (2019): 68–73. http://dx.doi.org/10.23968/1999-5571-2019-16-4-68-73.

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27

Domingue, John, and Dieter Fensel. "Problem solving methods in a global networked age." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23, no. 4 (October 14, 2009): 373–90. http://dx.doi.org/10.1017/s0890060409990060.

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AbstractWe believe that the future for problem solving method (PSM) derived work is very promising. In short, PSMs provide a solid foundation for creating a semantic layer supporting planetary-scale networks. Moreover, within a world-scale network where billions services are used and created by billions of parties inad hocdynamic fashion we believe that PSM-based mechanisms provide the only viable approach to dealing the sheer scale systematically. Our current experiments in this area are based upon a generic ontology for describing Web services derived from earlier work on PSMs. We outline how platforms based on our ontology can support large-scale networked interactivity in three main areas. Within a large European project we are able to map business level process descriptions to semantic Web service descriptions, to enable business experts to manage and use enterprise processes running in corporate information technology systems. Although highly successful, Web service-based applications predominately run behind corporate firewalls and are far less pervasive on the general Web. Within a second large European project we are extending our semantic service work using the principles underlying the Web and Web 2.0 to transform the Web from a Web of data to one where services are managed and used at large scale. Significant initiatives are now underway in North America, Asia, and Europe to design a new Internet using a “clean-slate” approach to fulfill the demands created by new modes of use and the additional 3 billion users linked to mobile phones. Our investigations within the European-based Future Internet program indicate that a significant opportunity exists for our PSM-derived work to address the key challenges currently identified: scalability, trust, interoperability, pervasive usability, and mobility. We outline one PSM-derived approach as an exemplar.

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28

Al-Homidan, Suliman. "Hybrid methods for solving the educational testing problem." Journal of Computational and Applied Mathematics 91, no. 1 (April 1998): 31–45. http://dx.doi.org/10.1016/s0377-0427(98)00011-9.

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29

Wang, F., W. Han, and X. Cheng. "Discontinuous Galerkin methods for solving the Signorini problem." IMA Journal of Numerical Analysis 31, no. 4 (May 23, 2011): 1754–72. http://dx.doi.org/10.1093/imanum/drr010.

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30

Capkovic, Frantisek. "DES modelling and control vs. problem solving methods." International Journal of Intelligent Information and Database Systems 1, no. 1 (2007): 53. http://dx.doi.org/10.1504/ijiids.2007.013285.

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31

Pescetti, D. "Dimensional analysis and qualitative methods in problem solving." European Journal of Physics 29, no. 4 (May 23, 2008): 697–707. http://dx.doi.org/10.1088/0143-0807/29/4/005.

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32

Wang, Fei. "Discontinuous Galerkin Methods for Solving Two Membranes Problem." Numerical Functional Analysis and Optimization 34, no. 2 (February 2013): 220–35. http://dx.doi.org/10.1080/01630563.2012.716805.

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33

Reynaud, Chantal, and Françoise Tort. "Using explicit ontologies to create problem solving methods." International Journal of Human-Computer Studies 46, no. 2-3 (February 1997): 339–64. http://dx.doi.org/10.1006/ijhc.1996.0095.

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34

TEN TEIJE, A., F. VAN HARMELEN, A. TH SCHREIBER, and B. J. WIELINGA. "Construction of problem-solving methods as parametric design." International Journal of Human-Computer Studies 49, no. 4 (October 1998): 363–89. http://dx.doi.org/10.1006/ijhc.1998.0211.

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35

Vabishchevich, P. N., and A. Yu Denisenko. "Numerical methods for solving the coefficient inverse problem." Computational Mathematics and Modeling 3, no. 3 (1992): 261–67. http://dx.doi.org/10.1007/bf01133895.

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36

Wang, Fei. "Discontinuous Galerkin methods for solving double obstacle problem." Numerical Methods for Partial Differential Equations 29, no. 2 (June 6, 2012): 706–20. http://dx.doi.org/10.1002/num.21730.

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37

Akcaoglu, Mete, Lucas J. Jensen, and Daisy Gonzalez. "Understanding Children’s Problem-solving Strategies in Solving Game-based Logic Problems." International Journal of Technology in Education and Science 5, no. 2 (March 17, 2021): 245–57. http://dx.doi.org/10.46328/ijtes.98.

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Problem solving is an essential skill for students to be successful in life and careers. Students need to use efficient strategies to solve problems effectively. In this basic interpretive qualitative study, we aimed to (a) explore children’s problem-solving strategies in a game-based tool (i.e., puzzles), and (b) investigate the troubleshooting strategies they employed while solving the puzzles. We recorded students’ puzzle-solving efforts, and using an observation analysis approach, noted important moments, patterns in puzzle-solving, troubleshooting methods, and other noteworthy events. Our analysis showed that while solving computer-based puzzles, students demonstrated the use of three approaches: varying-one-thing-at-a-time (VOTAT), building all-at-once or change-all (CA), and a mixed approach. CA was the approach used most often, followed by VOTAT, and then the mixed approach. Of the two troubleshooting approaches, starting the sequence over was the preferred method. Others opted to search for the faulty tile in the sequence. We discuss how these findings can inform practice and provide some insights as to the usefulness of the game-based tool, Lightbot.

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38

Annamalai, Nagappan, Shahrul Kamaruddin, Ishak Abdul Azid, and Ts Yeoh. "Problem Solving Methodology in Industry." Applied Mechanics and Materials 533 (February 2014): 510–15. http://dx.doi.org/10.4028/www.scientific.net/amm.533.510.

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This study presents the results of a literature review that was performed to identify and evaluate knowledge management such as problem solving (PS) methods are suitable for identification and analysis of risks on existing issues. The studied methods were compiled into 2 groups which is manufacturing, and research development. The key discussion would be where the PS tool is more relevant and how it help to solve the problem effectively. The aspects studied in the methods are presented together with a short description of its applications, area of the analysis and relevance to industry and education. Also some characteristics of the methods are given, as well as reference to previous key publications on the methods. The contribution of study is exploration on a new definition of PS methodology which is simplified and structured.

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Vladimirov, I. Yu, and A. V. Chistopolskaya. "Eye-tracking and cognitive monitoring as the methods of insight process objectification." Experimental Psychology (Russia) 12, no. 1 (2019): 167–79. http://dx.doi.org/10.17759/exppsy.2019120113.

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Current article highlights the results of the research of specific mechanisms of insight problem solving. It is based on the analysis of eye movement record data made by eye-tracker. The recorded data included average pupil diameter [mm] and fixation duration [ms]; the distribution of averaged eye movement values within the areas of interest during the manipulations with problem space was analyzed. The eye movement data was compared to the cognitive monitoring method data. The specificity of insight problems in comparison with non-insight (algorithmized) problems was validated. Several qualitative features of insight problem solving and the organization of problem space were revealed. Additionally, the priority of visual processing during insight problem solving was discovered: fixation duration increased in the “main problem” AOI.

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AUF'M HOFE, HARALD MEYER. "SOLVING ROSTERING TASKS BY GENERIC METHODS FOR CONSTRAINT OPTIMIZATION." International Journal of Foundations of Computer Science 12, no. 05 (October 2001): 671–93. http://dx.doi.org/10.1142/s0129054101000710.

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Based on experiences with the ORBIS·Dienstplan-system, this paper described how constraint processing can be used to implement automatic rostering systems. In practice, nurse rostering problems have many varying parameters. Hence, rostering requires a flexible formalism for representing the variants of the problem as well as a robust search procedure that is able to cope with all problem instances. On the one hand, the used constraint formalism allows the integration of very fine-grained optimization tasks by fuzzy constraints, which a roster may partially satisfy and partially violate. On the other hand, the described system uses an any-time algorithm to search good rosters. A meta-problem is generated from the constraint representation of the problem to identify reasons for constraint violations in order to improve the search for improvement steps.

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41

MOUHOUB, MALEK, and SAMIRA SADAOUI. "SOLVING INCREMENTAL SATISFIABILITY." International Journal on Artificial Intelligence Tools 16, no. 01 (February 2007): 139–47. http://dx.doi.org/10.1142/s0218213007003254.

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Propositional satisfiability (SAT) problem is fundamental to the theory of NP-completeness. Indeed, using the concept of "polynomial-time reducibility" all NP-complete problems can be polynomially reduced to SAT. Thus, any new technique for satisfiability problems will lead to general approaches for thousands of hard combinatorial problems. In this paper, we introduce the incremental propositional satisfiability problem that consists of maintaining the satisfiability of a propositional formula anytime a conjunction of new clauses is added. More precisely, the goal here is to check whether a solution to a SAT problem continues to be a solution anytime a new set of clauses is added and if not, whether the solution can be modified efficiently to satisfy the old formula and the new clauses. We will study the applicability of systematic and approximation methods for solving incremental SAT problems. The systematic method is based on the branch and bound technique while the approximation methods rely on stochastic local search and genetic algorithms. Experimental tests, conducted on randomly generated SAT instances, demonstrate the efficiency in time of the approximation methods over the branch and bound algorithm. However these approximation methods do not always guarantee the completeness of the solution returned. We show that a method we propose that uses non systematic search in a limited form together with branch and bound has the best compromise, in practice, between time and quality of the solution returned (success ratio).

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42

Agung, Chandra, and Natalia Christine. "PERFORMANCE ANALYSIS OF OPTIMIZATION METHODS FOR SOLVING TRAVELING SALESMAN PROBLEM." Innovative Technologies and Scientific Solutions for Industries, no. 1 (15) (March 31, 2021): 69–75. http://dx.doi.org/10.30837/itssi.2021.15.069.

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The subject of this research is distance and time of several city tour problems which known as traveling salesman problem (tsp). The goal is to find out the gaps of distance and time between two types of optimization methods in traveling salesman problem: exact and approximate. Exact method yields optimal solution but spends more time when the number of cities is increasing and approximate method yields near optimal solution even optimal but spends less time than exact methods. The task in this study is to identify and formulate each algorithm for each method, then to run each algorithm with the same input and to get the research output: total distance, and the last to compare both methods: advantage and limitation. Methods used are Brute Force (BF) and Branch and Bound (B&B) algorithms which are categorized as exact methods are compared with Artificial Bee Colony (ABC), Tabu Search (TS) and Simulated Annealing (SA) algorithms which are categorized as approximate methods or known as a heuristics method. These three approximate methods are chosen because they are effective algorithms, easy to implement and provide good solutions for combinatorial optimization problems. Exact and approximate algorithms are tested in several sizes of city tour problems: 6, 9, 10, 16, 17, 25, 42, and 58 cities. 17, 42 and 58 cities are derived from tsplib: a library of sample instances for tsp; and others are taken from big cities in Java (West, Central, East) island. All of the algorithms are run by MATLAB program. The results show that exact method is better in time performance for problem size less than 25 cities and both exact and approximate methods yield optimal solution. For problem sizes that have more than 25 cities, approximate method – Artificial Bee Colony (ABC) yields better time which is approximately 37% less than exact and deviates 0.0197% for distance from exact method. The conclusion is to apply exact method for problem size that is less than 25 cities and approximate method for problem size that is more than 25 cities. The gap of time will be increasing between two methods when sample size becomes larger.

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Gogodze, Joseph. "Ranking-Theory Methods for Solving Multicriteria Decision-Making Problems." Advances in Operations Research 2019 (April 1, 2019): 1–7. http://dx.doi.org/10.1155/2019/3217949.

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The Pareto optimality is a widely used concept for the multicriteria decision-making problems. However, this concept has a significant drawback—the set of Pareto optimal alternatives usually is large. Correspondingly, the problem of choosing a specific Pareto optimal alternative for the decision implementation is arising. This study proposes a new approach to select an “appropriate” alternative from the set of Pareto optimal alternatives. The proposed approach is based on ranking-theory methods used for ranking participants in sports tournaments. In the framework of the proposed approach, we build a special score matrix for a given multicriteria problem, which allows the use of the mentioned ranking methods and to choose the corresponding best-ranked alternative from the Pareto set as a solution of the problem. The proposed approach is particularly useful when no decision-making authority is available, or when the relative importance of various criteria has not been evaluated previously. The proposed approach is tested on an example of a materials-selection problem for a sailboat mast.

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44

Jones, Matthew, and Alison Megeney. "Thematic problem solving: a case study on an approach to teaching problem solving in undergraduate mathematics." MSOR Connections 17, no. 2 (April 24, 2019): 54. http://dx.doi.org/10.21100/msor.v17i2.978.

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Specialist mathematics, statistics and operational research (MSOR) programmes are recognised as intellectually demanding, and require students to formulate, abstract, and solve mathematical problems in a rigorous way. The process of developing the skills to do this well and communicate results can be challenging for learners as it requires a deep understanding of themes in mathematics as well as methods for solving problems. In this article we demonstrate how elements of Freudenthal’s Realistic Mathematics Education can be applied to teaching problem solving in undergraduate mathematics programmes. We describe an approach that moves away from standard practices and goes beyond problem solving methods to develop an understanding of common themes in mathematics.

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45

Mcguire, James, and Ruth Hatcher. "Offense-Focused Problem Solving." Criminal Justice and Behavior 28, no. 5 (October 2001): 564–87. http://dx.doi.org/10.1177/009385480102800502.

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Large-scale reviews of research on offender treatment have given clear indications that it is possible to reduce offender recidivism. Recently, a consensus has emerged concerning some of the features more likely to contribute to positive effects in this regard. One important consideration is that of focusing on criminogenic needs and employing methods designed to help offenders acquire cognitive problem-solving skills. In this article, a specially prepared group program is described, drawing on this research. The program has been run in probation service settings in the United Kingdom. In this preliminary report, short-term outcomes on intermediate treatment targets are presented for a sample of offenders ( n = 220) who completed the program. Significant pretest to posttest changes were found on variables associated with criminogenic attitudes. The results demonstrate the viability of providing a structured group program for delivery on a significant scale within community-based correctional services.

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46

Pearl Ghulam, Shenelle. "Teaching Electrolysis in a way that Enhances Problem Solving, Analytical and Communicative Skills." Lumat: International Journal of Math, Science and Technology Education 2, no. 2 (October 30, 2014): 182–88. http://dx.doi.org/10.31129/lumat.v2i2.1069.

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Problem solving is central in chemical education. In order to succeed as a chemistry student or professionally as a chemist it is highly crucial to develop skills that enhance problem solving. Prioritizing the development of problem solving a lesson on electrolysis was planned for 11th graders. This article presents significant teaching and learning methods that according to research enhance problem solving and other related skills. Additionally the article presents successful applications of these methods into teaching electrolysis concepts. The methods introduced are solving problems cooperatively in groups, solving problems with students rather than for students, the use of animations and provoking class participation and discussion.

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Gao, Yun Feng. "The Combined Homotopy Methods for Optimizition under the Quasi-Normal Cone Condition." Advanced Materials Research 459 (January 2012): 16–18. http://dx.doi.org/10.4028/www.scientific.net/amr.459.16.

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Non-convex programming problem is a hot problem in research field of optimization problems, since the interior point method is applied for solving programming problem. In this paper, we use the homotopy interior point method for solving a class of optimization problems by the existing theoretical results under quasi-norm cone condition. Contrary to this partial reverse convex constrained domain, we give the structure method of the quasi-norm cone condition, construct the combined homotopy method under quasi-norm cone condition and show some numerical examples

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Sardar Babayeva, Ruqayya. "Methods of teaching type problem solving in the elementary course of mathematics." SCIENTIFIC WORK 56, no. 07 (August 4, 2020): 89–92. http://dx.doi.org/10.36719/2663-4619/56/89-92.

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Complex arithmetic problems are solved by two or more operations. Complex issues different in their structure and the relationship between quantities. When problems differ in their dependence on quantities, they are assigned to different groups. Such problems are called type problems, and each type of problem has its own solution. There can be different types of issues related to the same quantity. Before solving any type of problem, a preparation problem should be used to facilitate its solution. The issue of preparation should be resolved relatively easily and orally. Students need to be taught to compare when looking for solutions to typical problems. Key words:Complicated matter,price,unknown,quantity,numerical price,method

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Kavrin, D. А., and S. A. Subbotin. "THE METHODS FOR QUANTITATIVE SOLVING THE CLASS IMBALANCE PROBLEM." Radio Electronics, Computer Science, Control, no. 1 (May 29, 2018): 83–90. http://dx.doi.org/10.15588/1607-3274-2018-1-10.

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Zhang, Hao Lan, and Hoong Chuin Lau. "Agent-based problem solving methods in Big Data environment." Web Intelligence and Agent Systems: An International Journal 12, no. 4 (2014): 343–45. http://dx.doi.org/10.3233/wia-140300.

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