000			07643cam a2200637Ia 4500
001			ocn227117250
003			OCoLC
005			20141103172212.0
006			m o d
007			cr bn|||||||||
008			080506s2005 ne ob 001 0 eng d
040			_aUCW _beng _cUCW _dN$T _dYDXCP _dMERUC _dUBY _dE7B _dOCLCQ _dIDEBK _dOCLCQ _dOPELS _dTEF _dOCLCQ _dOCLCF
019			_a75190863 _a144220175 _a441764166 _a505057892 _a647545753 _a772910106
020			_a0444515070 (hbk.)
020			_a9780444515070 (hbk.)
020			_a0080459218 (electronic bk.)
020			_a9780080459219 (electronic bk.)
024	3		_z9780444515070
029	1		_aNZ1 _b14675976
029	1		_aDEBSZ _b414263243
035			_a(OCoLC)227117250 _z(OCoLC)75190863 _z(OCoLC)144220175 _z(OCoLC)441764166 _z(OCoLC)505057892 _z(OCoLC)647545753 _z(OCoLC)772910106
072		7	_aMAT _x042000 _2bisacsh
082	0	4	_a519.6 _222
090			_aQA402.5 _b.D57 2005a
049			_aTEFA
245	0	0	_aDiscrete optimization _h[electronic resource] / _cedited by K. Aardal, G.L. Nemhauser and R. Weismantel.
250			_a1st ed.
260			_aAmsterdam ; _aBoston : _bElsevier, _c2005.
300			_a1 online resource (xi, 607 p.)
490	1		_aHandbooks in operations research and management science, _x0927-0507 ; _vv. 12
588			_aDescription based on print version record.
504			_aIncludes bibliographical references and index.
505	0		_aOn the history of combinatorial optimization (till 1960) -- Computational integer programming and cutting planes -- The structure of group relaxations -- Integer programming, lattices, and results in fixed dimension -- Primal integer programming -- Balanced matrices -- Submodular function minimization -- Semidefinite programming and integer programming -- Algorithms for stochastic mixed-integer programming models -- Constraint programming.
520			_aThe chapters of this Handbook volume covers nine main topics that are representative of recent theoretical and algorithmic developments in the field. In addition to the nine papers that present the state of the art, there is an article on the early history of the field. The handbook will be a useful reference to experts in the field as well as students and others who want to learn about discrete optimization. All of the chapters in this handbook are written by authors who have made significant original contributions to their topics. Herewith a brief introduction to the chapters of the handbook. "On the history of combinatorial optimization (until 1960)" goes back to work of Monge in the 18th century on the assignment problem and presents six problem areas: assignment, transportation, maximum flow, shortest tree, shortest path and traveling salesman. The branch-and-cut algorithm of integer programming is the computational workhorse of discrete optimization. It provides the tools that have been implemented in commercial software such as CPLEX and Xpress MP that make it possible to solve practical problems in supply chain, manufacturing, telecommunications and many other areas. "Computational integer programming and cutting planes" presents the key ingredients of these algorithms. Although branch-and-cut based on linear programming relaxation is the most widely used integer programming algorithm, other approaches are needed to solve instances for which branch-and-cut performs poorly and to understand better the structure of integral polyhedra. The next three chapters discuss alternative approaches. "The structure of group relaxations" studies a family of polyhedra obtained by dropping certain nonnegativity restrictions on integer programming problems. Although integer programming is NP-hard in general, it is polynomially solvable in fixed dimension. "Integer programming, lattices, and results in fixed dimension" presents results in this area including algorithms that use reduced bases of integer lattices that are capable of solving certain classes of integer programs that defy solution by branch-and-cut. Relaxation or dual methods, such as cutting plane algorithms,progressively remove infeasibility while maintaining optimality to the relaxed problem. Such algorithms have the disadvantage of possibly obtaining feasibility only when the algorithm terminates.Primal methods for integer programs, which move from a feasible solution to a better feasible solution, were studied in the 1960's but did not appear to be competitive with dual methods. However,recent development in primal methods presented in "Primal integer programming" indicate that this approach is not just interesting theoretically but may have practical implications as well. The study of matrices that yield integral polyhedra has a long tradition in integer programming. A major breakthrough occurred in the 1990's with the development of polyhedral and structural results and recognition algorithms for balanced matrices. "Balanced matrices" is a tutorial on the subject. Submodular function minimization generalizes some linear combinatorial optimization problems such as minimum cut and is one of the fundamental problems of the field that is solvable in polynomial time. "Submodular function minimization" presents the theory and algorithms of this subject. In the search for tighter relaxations of combinatorial optimization problems, semidefinite programming provides a generalization of linear programming that can give better approximations and is still polynomially solvable. This subject is discussed in "Semidefinite programming and integer programming". Many real world problems have uncertain data that is known only probabilistically. Stochastic programming treats this topic, but until recently it was limited, for computational reasons, to stochastic linear programs. Stochastic integer programming is now a high profile research area and recent developments are presented in "Algorithms for stochastic mixed-integer programming models". Resource constrained scheduling is an example of a class of combinatorial optimization problems that is not naturally formulated with linear constraints so that linear programming based methods do not work well. "Constraint programming" presents an alternative enumerative approach that is complementary to branch-and-cut. Constraint programming,primarily designed for feasibility problems, does not use a relaxation to obtain bounds. Instead nodes of the search tree are pruned by constraint propagation, which tightens bounds on variables until their values are fixed or their domains are shown to be empty.
650		0	_aMathematical optimization.
650		0	_aInteger programming.
650		7	_aMATHEMATICS _xOptimization. _2bisacsh
650		6	_aOptimisation math�ematique.
650		6	_aProgrammation en nombres entiers.
650	0	7	_aDiskrete Optimierung. _2swd
650		7	_aInteger programming. _2fast _0(OCoLC)fst00975500
650		7	_aMathematical optimization. _2fast _0(OCoLC)fst01012099
655		4	_aElectronic books.
700	1		_aAardal, K. _q(Karen) _4edt
700	1		_aNemhauser, George L. _4edt
700	1		_aWeismantel, Robert. _4edt
776	0	8	_iPrint version: _tDiscrete optimization. _b1st ed. _dAmsterdam ; Boston : Elsevier, 2005 _w(OCoLC)56875489
830		0	_aHandbooks in operations research and management science ; _vv. 12. _x0927-0507
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780444515070
938			_aYBP Library Services _bYANK _n2487445
938			_aebrary _bEBRY _nebr10138290
938			_aIngram Digital eBook Collection _bIDEB _n63811
938			_aEBSCOhost _bEBSC _n166624
942			_cEB
994			_aC0 _bTEF
999			_c21009 _d21009