001434107 000__ 03312cam\a2200565\i\4500
001434107 001__ 1434107
001434107 003__ OCoLC
001434107 005__ 20230309003710.0
001434107 006__ m\\\\\o\\d\\\\\\\\
001434107 007__ cr\cn\nnnunnun
001434107 008__ 210221s2021\\\\sz\\\\\\ob\\\\001\0\eng\d
001434107 019__ $$a1239991546$$a1244118550$$a1269104480$$a1284942438
001434107 020__ $$a9783030623913$$q(electronic bk.)
001434107 020__ $$a3030623912$$q(electronic bk.)
001434107 020__ $$z3030623890
001434107 020__ $$z9783030623890
001434107 0247_ $$a10.1007/978-3-030-62391-3$$2doi
001434107 035__ $$aSP(OCoLC)1237966923
001434107 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dYDXIT$$dOCLCO$$dGW5XE$$dDCT$$dEBLCP$$dOCLCF$$dN$T$$dSFB$$dOCLCQ$$dOCLCO$$dOCLCQ
001434107 049__ $$aISEA
001434107 050_4 $$aQA649$$b.S38 2021
001434107 08204 $$a516.373$$223
001434107 1001_ $$aSato, Hiroyuki,$$eauthor.
001434107 24510 $$aRiemannian optimization and its applications /$$cHiroyuki Sato.
001434107 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001434107 300__ $$a1 online resource
001434107 336__ $$atext$$btxt$$2rdacontent
001434107 337__ $$acomputer$$bc$$2rdamedia
001434107 338__ $$aonline resource$$bcr$$2rdacarrier
001434107 347__ $$atext file
001434107 347__ $$bPDF
001434107 4901_ $$aSpringerBriefs in electrical and computer engineering. Control, automation and robotics
001434107 504__ $$aIncludes bibliographical references and index.
001434107 5050_ $$aChapter 1. Introduction -- Chapter 2. Unconstrained Optimization on Riemannian Manifolds -- Chapter 3. Conjugate Gradient Methods on Manifolds -- Chapter 4. Applications of Riemannian Optimization.
001434107 506__ $$aAccess limited to authorized users.
001434107 520__ $$aThis brief describes the basics of Riemannian optimization--optimization on Riemannian manifolds--introduces algorithms for Riemannian optimization problems, discusses the theoretical properties of these algorithms, and suggests possible applications of Riemannian optimization to problems in other fields. To provide the reader with a smooth introduction to Riemannian optimization, brief reviews of mathematical optimization in Euclidean spaces and Riemannian geometry are included. Riemannian optimization is then introduced by merging these concepts. In particular, the Euclidean and Riemannian conjugate gradient methods are discussed in detail. A brief review of recent developments in Riemannian optimization is also provided. Riemannian optimization methods are applicable to many problems in various fields. This brief discusses some important applications including the eigenvalue and singular value decompositions in numerical linear algebra, optimal model reduction in control engineering, and canonical correlation analysis in statistics.
001434107 588__ $$aOnline resource; title from digital title page (viewed on March 24, 2021).
001434107 650_0 $$aRiemannian manifolds.
001434107 650_0 $$aMathematical optimization.
001434107 650_6 $$aVariétés de Riemann.
001434107 650_6 $$aOptimisation mathématique.
001434107 655_0 $$aElectronic books.
001434107 77608 $$iPrint version:$$z3030623890$$z9783030623890$$w(OCoLC)1197841499
001434107 830_0 $$aSpringerBriefs in electrical and computer engineering.$$pControl, automation and robotics.
001434107 852__ $$bebk
001434107 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-62391-3$$zOnline Access$$91397441.1
001434107 909CO $$ooai:library.usi.edu:1434107$$pGLOBAL_SET
001434107 980__ $$aBIB
001434107 980__ $$aEBOOK
001434107 982__ $$aEbook
001434107 983__ $$aOnline
001434107 994__ $$a92$$bISE