{"title":"雌鹿MIParray:一个用于正交阵列算法创建的R包","authors":"U. Grömping","doi":"10.5334/jors.286","DOIUrl":null,"url":null,"abstract":"The R package DoE.MIParray uses mixed integer optimization for creating well-balanced arrays for experimental designs. Its use requires availability of at least one of the commercial optimizers Gurobi or Mosek. Investing some effort into the creation of a suitable array is justified, because experimental runs are often very expensive, so that their information content should be maximized. DoE.MIParray is particularly useful for creating relatively small mixed level designs. Balance is optimized by applying the quality criterion “generalized minimum aberration” (GMA), which aims at minimizing confounding of low order effects in factorial models, without assuming a specific model. For relevant cases, DoE.MIParray exploits a lower bound on its objective function, which allows to drastically reduce the computational burden of mixed integer optimization.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DoE.MIParray: An R Package for Algorithmic Creation of Orthogonal Arrays\",\"authors\":\"U. Grömping\",\"doi\":\"10.5334/jors.286\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The R package DoE.MIParray uses mixed integer optimization for creating well-balanced arrays for experimental designs. Its use requires availability of at least one of the commercial optimizers Gurobi or Mosek. Investing some effort into the creation of a suitable array is justified, because experimental runs are often very expensive, so that their information content should be maximized. DoE.MIParray is particularly useful for creating relatively small mixed level designs. Balance is optimized by applying the quality criterion “generalized minimum aberration” (GMA), which aims at minimizing confounding of low order effects in factorial models, without assuming a specific model. For relevant cases, DoE.MIParray exploits a lower bound on its objective function, which allows to drastically reduce the computational burden of mixed integer optimization.\",\"PeriodicalId\":37323,\"journal\":{\"name\":\"Journal of Open Research Software\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Open Research Software\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5334/jors.286\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Open Research Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/jors.286","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
DoE.MIParray: An R Package for Algorithmic Creation of Orthogonal Arrays
The R package DoE.MIParray uses mixed integer optimization for creating well-balanced arrays for experimental designs. Its use requires availability of at least one of the commercial optimizers Gurobi or Mosek. Investing some effort into the creation of a suitable array is justified, because experimental runs are often very expensive, so that their information content should be maximized. DoE.MIParray is particularly useful for creating relatively small mixed level designs. Balance is optimized by applying the quality criterion “generalized minimum aberration” (GMA), which aims at minimizing confounding of low order effects in factorial models, without assuming a specific model. For relevant cases, DoE.MIParray exploits a lower bound on its objective function, which allows to drastically reduce the computational burden of mixed integer optimization.