{"title":"问题和解决者的基准:一种博弈论方法","authors":"Joseph Gogodze","doi":"10.2478/fcds-2019-0008","DOIUrl":null,"url":null,"abstract":"Abstract In this note, we propose a game-theoretic approach for benchmarking computational problems and their solvers. The approach takes an assessment matrix as a payoff matrix for some zero-sum matrix game in which the first player chooses a problem and the second player chooses a solver. The solution in mixed strategies of this game is used to construct a notionally objective ranking of the problems and solvers under consideration. The proposed approach is illustrated in terms of an example to demonstrate its viability and its suitability for applications.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"44 1","pages":"137 - 150"},"PeriodicalIF":1.8000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Benchmarking of Problems and Solvers: a Game-Theoretic Approach\",\"authors\":\"Joseph Gogodze\",\"doi\":\"10.2478/fcds-2019-0008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In this note, we propose a game-theoretic approach for benchmarking computational problems and their solvers. The approach takes an assessment matrix as a payoff matrix for some zero-sum matrix game in which the first player chooses a problem and the second player chooses a solver. The solution in mixed strategies of this game is used to construct a notionally objective ranking of the problems and solvers under consideration. The proposed approach is illustrated in terms of an example to demonstrate its viability and its suitability for applications.\",\"PeriodicalId\":42909,\"journal\":{\"name\":\"Foundations of Computing and Decision Sciences\",\"volume\":\"44 1\",\"pages\":\"137 - 150\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Foundations of Computing and Decision Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/fcds-2019-0008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations of Computing and Decision Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/fcds-2019-0008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Benchmarking of Problems and Solvers: a Game-Theoretic Approach
Abstract In this note, we propose a game-theoretic approach for benchmarking computational problems and their solvers. The approach takes an assessment matrix as a payoff matrix for some zero-sum matrix game in which the first player chooses a problem and the second player chooses a solver. The solution in mixed strategies of this game is used to construct a notionally objective ranking of the problems and solvers under consideration. The proposed approach is illustrated in terms of an example to demonstrate its viability and its suitability for applications.