Mixed Integer Nonlinear Programming

Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINL...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Lee, Jon (Επιμελητής έκδοσης), Leyffer, Sven (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: New York, NY : Springer New York, 2012.
Σειρά:The IMA Volumes in Mathematics and its Applications, 154
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 04051nam a22004935i 4500
001 978-1-4614-1927-3
003 DE-He213
005 20151125231235.0
007 cr nn 008mamaa
008 111201s2012 xxu| s |||| 0|eng d
020 |a 9781461419273  |9 978-1-4614-1927-3 
024 7 |a 10.1007/978-1-4614-1927-3  |2 doi 
040 |d GrThAP 
050 4 |a QA401-425 
072 7 |a PBKJ  |2 bicssc 
072 7 |a MAT034000  |2 bisacsh 
082 0 4 |a 511.4  |2 23 
245 1 0 |a Mixed Integer Nonlinear Programming  |h [electronic resource] /  |c edited by Jon Lee, Sven Leyffer. 
264 1 |a New York, NY :  |b Springer New York,  |c 2012. 
300 |a XX, 692 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a The IMA Volumes in Mathematics and its Applications,  |x 0940-6573 ;  |v 154 
505 0 |a Foreword -- Preface.-Algorithms and software for convex mixed integer nonlinearprograms.-  Subgradient based outer approximation for mixed integer secondorder cone programming.-Perspective reformulation and applications -- Generalized disjunctive programming: A framework for formulation and alternative algorithms for MINLP optimization.-Disjunctive cuts for nonconvex MINLP -- Sequential quadratic programming methods -- Using interior-point methods within an outer approximation framework for mixed integer nonlinear programming -- Using expression graphs in optimization algorithms -- Symmetry in mathematical programming -- Using piecewise linear functions for solving MINLPs -- An algorithmic framework for MINLP with separable non-convexity -- Global optimization of mixed-integer signomial programming problems.-The MILP road to MIQCP -- Linear programming relaxations of quadratically constrained quadratic programs -- Extending a CIP framework to solve MIQCPs -- Computation with polynomial equations and inequalities arisingin combinatorial optimization.-  Matrix relaxations in combinatorial optimization -- A polytope for a product of real linear functions in 0/1 variables -- On the complexity of nonlinear mixed-integer optimization -- Theory and applications of n-fold integer programming -- MINLP Application for ACH interiors restructuring -- A benchmark library of mixed-integer optimal control problems. 
520 |a Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances. 
650 0 |a Mathematics. 
650 0 |a Approximation theory. 
650 0 |a Algorithms. 
650 0 |a Mathematical optimization. 
650 1 4 |a Mathematics. 
650 2 4 |a Approximations and Expansions. 
650 2 4 |a Algorithms. 
650 2 4 |a Continuous Optimization. 
700 1 |a Lee, Jon.  |e editor. 
700 1 |a Leyffer, Sven.  |e editor. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781461419266 
830 0 |a The IMA Volumes in Mathematics and its Applications,  |x 0940-6573 ;  |v 154 
856 4 0 |u http://dx.doi.org/10.1007/978-1-4614-1927-3  |z Full Text via HEAL-Link 
912 |a ZDB-2-SMA 
950 |a Mathematics and Statistics (Springer-11649)