000			02178nam a22002297a 4500
999			_c1368 _d1368
005			20211221113620.0
008			211221b ||||| |||| 00| 0 eng d
020			_a9789353066369
082			_a519.7 _bRAR
100			_aRardin, Ronald L. _94477
245			_aOptimization in operations research
250			_a2nd
260			_bPearson India Education Services Pvt. Ltd. _aNew Delhi _c2019
300			_axxviii, 1144 p.
365			_aINR _b969.00
504			_aTable of Content 1: Problem Solving with Mathematical Models 2: Deterministic Optimization Models in Operations Research 3: Improving Search 4: Linear Programming Models 5: Simplex Search for Linear Programming 6: Duality, Sensitivity, and Optimality in Linear Programming 7: Interior Point Methods for Linear Programming 8: Multiobjective Optimization and Goal Programming 9: Shortest Paths and Discrete Dynamic Programming 10: Network Flows and Graphs 11: Discrete Optimization Models 12: Exact Discrete Optimization Methods 13: Large-Scale Optimization Methods 14: Computational Complexity Theory 15: Heuristic Methods for Approximate Discrete Optimization 16: Unconstrained Nonlinear Programming 17: Constrained Nonlinear Programming "
520			_a The goal of the Second Edition is to make the tools of optimization modeling and analysis even more widely accessible to advanced undergraduate and beginning graduate students, as well as to researchers and working practitioners who use it as a reference for self-study. The emphasis lies in developing skills and intuitions that students can apply in real settings or later coursework. Like the first, the Second Edition covers the full scope of optimization (mathematical programming), spanning linear, integer, nonlinear, network, and dynamic programming models and algorithms, in both single and multiobjective contexts. New material adds large-scale, stochastic and complexity topics, while broadly deepening mathematical rigor without sacrificing the original's intuitive style.
650			_aMathematical optimization _9647
650			_aOperations research _9757
650			_aProgramming (Mathematics) _94478
942			_2ddc _cBK