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تعداد نتایج: 363

Numerical Optimization

By Jorge Nocedal, Stephen Wright
Publisher: Springer Science & Business Media, 2006-12-11
Pages: 686
Language: en
ISBN_13:9780387400655
ISBN_10:0387400656
Subject: Mathematics

Bird Conservation International Special Issue: Neotropical Migrants

Growing Points in Neotropical Migratory Bird Conservation Pa...

جزئیات و درخواست

By Eugene S. Morton, Kimberley Young, Mario A. Ramos
Publisher: Cambridge University Press, 1994-08-04
Pages: 204
Language: en
ISBN_10:0521478766
ISBN_13:9780521478762
Subject: Nature

Stochastic Optimization for Large-scale Machine Learning

جزئیات و درخواست

By Vinod Kumar Chauhan
Publisher: CRC Press, 2021-11-18
Pages: 189
Language: en
ISBN_13:9781000505610
ISBN_10:1000505618
Subject: Computers

Echtzeitfähige 3D Posenbestimmung des Menschen in der Robotik

Methoden und Anwendungen

جزئیات و درخواست

By Kristian Ehlers
Publisher: Springer-Verlag, 2018-12-28
Pages: 270
Language: de
ISBN_13:9783658248222
ISBN_10:365824822X
Subject: Computers

Reflektierte algorithmische Textanalyse

Interdisziplinäre(s) Arbeiten in der CRETA-Werkstatt

جزئیات و درخواست

By Nils Reiter, Axel Pichler, Jonas Kuhn
Publisher: Walter de Gruyter GmbH & Co KG, 2020-07-20
Pages: 490
Language: de
ISBN_13:9783110694024
ISBN_10:3110694026
Subject: Philosophy

Optimierung für Wirtschaftsingenieure

جزئیات و درخواست

By Martin Bünner
Publisher: Springer-Verlag, 2019-08-30
Pages: 156
Language: de
ISBN_13:9783658266103
ISBN_10:3658266104
Subject: Mathematics

Fahrzeugeigenlokalisierung innerhalb bekannter Indoor-Umgebungen mittels Seriensensorik

جزئیات و درخواست

By Stefan Stümper
Publisher: Cuvillier Verlag, 2016-01-13
Pages: 182
Language: de
ISBN_13:9783736981706
ISBN_10:3736981708
Subject: Computers

Numerical Optimization

جزئیات و درخواست

By Jorge Nocedal, Stephen Wright
Publisher: Springer Science & Business Media, 2006-06-06
Pages: 651
Language: en
ISBN_13:9780387227429
ISBN_10:0387227423
Subject: Mathematics

Optimization Software Guide

جزئیات و درخواست

By Jorge J. More, Stephen J. Wright
Publisher: SIAM, 1993-01-01
Pages: 149
Language: en
ISBN_13:9780898713220
ISBN_10:0898713226
Subject: Mathematics

Large-Scale Nonlinear Optimization

جزئیات و درخواست

By Gianni Pillo, Massimo Roma
Publisher: Springer Science & Business Media, 2006-06-03
Pages: 297
Language: en
ISBN_13:9780387300658
ISBN_10:0387300651
Subject: Mathematics

Deep Learning at Scale

جزئیات و درخواست

By Suneeta Mall
Publisher: "O'Reilly Media, Inc.", 2024-06-18
Pages: 404
Language: en
ISBN_13:9781098145248
ISBN_10:1098145240
Subject: Computers

By Bertrand Braunschweig, Rafiqul Gani
Publisher: Elsevier, 2002-10-30
Pages: 713
Language: en
ISBN_13:9780080541365
ISBN_10:0080541364
Subject: Computers

Data-Intensive Text Processing with MapReduce

جزئیات و درخواست

By Jimmy Lin, Chris Dyer
Publisher: Springer Nature, 2022-05-31
Pages: 171
Language: en
ISBN_13:9783031021367
ISBN_10:3031021363
Subject: Computers

System Modelling and Optimization

Methods, Theory and Applications. 19th IFIP TC7 Conference o...

جزئیات و درخواست

By M.J.D. Powell, S. Scholtes
Publisher: Springer, 2013-03-20
Pages: 352
Language: en
ISBN_13:9780387355146
ISBN_10:0387355146
Subject: Technology & Engineering

Advances in Nonlinear Programming

Proceedings of the 96 International Conference on Nonlinear ...

جزئیات و درخواست

By Ya-xiang Yuan
Publisher: Springer Science & Business Media, 2013-12-01
Pages: 353
Language: en
ISBN_13:9781461333357
ISBN_10:1461333350
Subject: Mathematics

ECAI 2023

26th European Conference on Artificial Intelligence, Septemb...

جزئیات و درخواست

By K. Gal, A. Nowé, G.J. Nalepa
Publisher: IOS Press, 2023-10-18
Pages: 3328
Language: en
ISBN_13:9781643684376
ISBN_10:164368437X
Subject: Computers

Numerical Optimization 1984

Proceedings of the SIAM Conference on Numerical Optimization...

جزئیات و درخواست

By Paul T. Boggs, Richard H. Byrd, Robert B. Schnabel
Publisher: SIAM, 1985-01-01
Pages: 310
Language: en
ISBN_10:0898710545
ISBN_13:9780898710540
Subject: Mathematics

Convex Optimization & Euclidean Distance Geometry

$The study of Euclidean distance matrices (EDMs) fundamentally asks what can be known geometrically given onlydistance information between points in Euclidean space. Each point may represent simply locationor, abstractly, any entity expressible as a vector in finite-dimensional Euclidean space.The answer to the question posed is that very much can be known about the points;the mathematics of this combined study of geometry and optimization is rich and deep.Throughout we cite beacons of historical accomplishment.The application of EDMs has already proven invaluable in discerning biological molecular conformation.The emerging practice of localization in wireless sensor networks, the global positioning system (GPS), and distance-based pattern recognitionwill certainly simplify and benefit from this theory.We study the pervasive convex Euclidean bodies and their various representations.In particular, we make convex polyhedra, cones, and dual cones more visceral through illustration, andwe study the geometric relation of polyhedral cones to nonorthogonal bases biorthogonal expansion.We explain conversion between halfspace- and vertex-descriptions of convex cones,we provide formulae for determining dual cones,and we show how classic alternative systems of linear inequalities or linear matrix inequalities and optimality conditions can be explained by generalized inequalities in terms of convex cones and their duals.The conic analogue to linear independence, called conic independence, is introducedas a new tool in the study of classical cone theory; the logical next step in the progression:linear, affine, conic.Any convex optimization problem has geometric interpretation.This is a powerful attraction: the ability to visualize geometry of an optimization problem.We provide tools to make visualization easier.The concept of faces, extreme points, and extreme directions of convex Euclidean bodiesis explained here, crucial to understanding convex optimization.The convex cone of positive semidefinite matrices, in particular, is studied in depth.We mathematically interpret, for example,its inverse image under affine transformation, and we explainhow higher-rank subsets of its boundary united with its interior are convex.The Chapter on "Geometry of convex functions",observes analogies between convex sets and functions:The set of all vector-valued convex functions is a closed convex cone.Included among the examples in this chapter, we show how the real affinefunction relates to convex functions as the hyperplane relates to convex sets.Here, also, pertinent results formultidimensional convex functions are presented that are largely ignored in the literature;tricks and tips for determining their convexityand discerning their geometry, particularly with regard to matrix calculus which remains largely unsystematizedwhen compared with the traditional practice of ordinary calculus.Consequently, we collect some results of matrix differentiation in the appendices.The Euclidean distance matrix (EDM) is studied,its properties and relationship to both positive semidefinite and Gram matrices.We relate the EDM to the four classical axioms of the Euclidean metric;thereby, observing the existence of an infinity of axioms of the Euclidean metric beyondthe triangle inequality. We proceed byderiving the fifth Euclidean axiom and then explain why furthering this endeavoris inefficient because the ensuing criteria (while describing polyhedra)grow linearly in complexity and number.Some geometrical problems solvable via EDMs,EDM problems posed as convex optimization, and methods of solution arepresented;\eg, we generate a recognizable isotonic map of the United States usingonly comparative distance information (no distance information, only distance inequalities).We offer a new proof of the classic Schoenberg criterion, that determines whether a candidate matrix is an EDM. Our proofrelies on fundamental geometry; assuming, any EDM must correspond to a list of points contained in some polyhedron(possibly at its vertices) and vice versa.It is not widely known that the Schoenberg criterion implies nonnegativity of the EDM entries; proved here.We characterize the eigenvalues of an EDM matrix and then devisea polyhedral cone required for determining membership of a candidate matrix(in Cayley-Menger form) to the convex cone of Euclidean distance matrices (EDM cone); \ie,a candidate is an EDM if and only if its eigenspectrum belongs to a spectral cone for EDM^N.We will see spectral cones are not unique.In the chapter "EDM cone", we explain the geometric relationship betweenthe EDM cone, two positive semidefinite cones, and the elliptope.We illustrate geometric requirements, in particular, for projection of a candidate matrixon a positive semidefinite cone that establish its membership to the EDM cone. The faces of the EDM cone are described,but still open is the question whether all its faces are exposed as they are for the positive semidefinite cone.The classic Schoenberg criterion, relating EDM and positive semidefinite cones, isrevealed to be a discretized membership relation (a generalized inequality, a new Farkas''''''''-like lemma)between the EDM cone and its ordinary dual. A matrix criterion for membership to the dual EDM cone is derived thatis simpler than the Schoenberg criterion.We derive a new concise expression for the EDM cone and its dual involvingtwo subspaces and a positive semidefinite cone."Semidefinite programming" is reviewedwith particular attention to optimality conditionsof prototypical primal and dual conic programs,their interplay, and the perturbation method of rank reduction of optimal solutions(extant but not well-known).We show how to solve a ubiquitous platonic combinatorial optimization problem from linear algebra(the optimal Boolean solution x to Ax=b)via semidefinite program relaxation.A three-dimensional polyhedral analogue for the positive semidefinite cone of 3X3 symmetricmatrices is introduced; a tool for visualizing in 6 dimensions.In "EDM proximity"we explore methods of solution to a few fundamental and prevalentEuclidean distance matrix proximity problems; the problem of finding that Euclidean distance matrix closestto a given matrix in the Euclidean sense.We pay particular attention to the problem when compounded with rank minimization.We offer a new geometrical proof of a famous result discovered by Eckart \& Young in 1936 regarding Euclideanprojection of a point on a subset of the positive semidefinite cone comprising all positive semidefinite matriceshaving rank not exceeding a prescribed limit rho.We explain how this problem is transformed to a convex optimization for any rank rho.$

جزئیات و درخواست

By Jon Dattorro
Publisher: Meboo Publishing USA, 2005
Pages: 776
Language: en
ISBN_13:9780976401308
ISBN_10:0976401304
Subject: Mathematics

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