01353cam a22003253a 450099100296216970753620070928145729.0 97898127053969812705392b13608538-39ule_inst591.1/88/015119LC QP356574510.92Tirozzi, Brunello552792Introduction to computational neurobiology and clustering /Brunello Tirozzi, Daniela Bianchi, Enrico FerraroSingapore :World Scientific,c2007xii, 229 p. :ill. ;24 cmSeries on advances in mathematics for applied sciences ;v. 73Includes bibliographical references and indexNeurobiologyMathematical modelsBianchi, Danielaauthorhttp://id.loc.gov/vocabulary/relators/aut251549Ferraro, Enricoauthorhttp://id.loc.gov/vocabulary/relators/aut738838.b1360853819-10-0719-10-07991002962169707536LE006 510.90/510.93 TIR12006000160858le006pE47.39-l- 00000.i1458514519-10-07Introduction to computational neurobiology and clustering1463579UNISALENTOle00619-10-07ma -engsi 0002071nam 2200409 450 991083075550332120200610221238.81-119-61240-31-119-61247-01-119-61236-5(CKB)4100000007934807(MiAaPQ)EBC5748879(CaSebORM)9781786304094(EXLCZ)99410000000793480720190427d2019 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierIterative optimizers difficulty measures and benchmarks /Maurice Clerc1st editionHoboken, New Jersey :ISTE :Wiley,2019.1 online resource (215 pages)1-78630-409-0 Almost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.Mathematical optimizationMathematical optimization.519.3 Clerc Maurice845965MiAaPQMiAaPQMiAaPQBOOK9910830755503321Iterative optimizers4065263UNINA