{"title":"代价函数对s参数优化空间的影响","authors":"D. de Araujo, J. Pingenot","doi":"10.1109/EPEPS.2017.8329742","DOIUrl":null,"url":null,"abstract":"Optimization algorithms require a cost function in order to quantitatively compare results and reach convergence to the maxima/minima desired. The choice of cost function can significantly affect the complexity of the optimization space. This work investigates the impact of cost function of S-parameters on the optimization solution space.","PeriodicalId":397179,"journal":{"name":"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cost function impact on S-parameter optimization space\",\"authors\":\"D. de Araujo, J. Pingenot\",\"doi\":\"10.1109/EPEPS.2017.8329742\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Optimization algorithms require a cost function in order to quantitatively compare results and reach convergence to the maxima/minima desired. The choice of cost function can significantly affect the complexity of the optimization space. This work investigates the impact of cost function of S-parameters on the optimization solution space.\",\"PeriodicalId\":397179,\"journal\":{\"name\":\"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)\",\"volume\":\"110 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EPEPS.2017.8329742\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPEPS.2017.8329742","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cost function impact on S-parameter optimization space
Optimization algorithms require a cost function in order to quantitatively compare results and reach convergence to the maxima/minima desired. The choice of cost function can significantly affect the complexity of the optimization space. This work investigates the impact of cost function of S-parameters on the optimization solution space.