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BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems 2015 ed. [Minkštas viršelis]

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  • Formatas: Paperback / softback, 146 pages, aukštis x plotis: 235x155 mm, weight: 2584 g, 19 Illustrations, color; 38 Illustrations, black and white; XVIII, 146 p. 57 illus., 19 illus. in color., 1 Paperback / softback
  • Serija: SpringerBriefs in Optimization
  • Išleidimo metai: 06-Mar-2015
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 1493922815
  • ISBN-13: 9781493922819
Kitos knygos pagal šią temą:
BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems 2015 ed.Didesnis paveikslėlis
  • Formatas: Paperback / softback, 146 pages, aukštis x plotis: 235x155 mm, weight: 2584 g, 19 Illustrations, color; 38 Illustrations, black and white; XVIII, 146 p. 57 illus., 19 illus. in color., 1 Paperback / softback
  • Serija: SpringerBriefs in Optimization
  • Išleidimo metai: 06-Mar-2015
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 1493922815
  • ISBN-13: 9781493922819
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781493922819.html
Raktažodžiai:
This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.

Recenzijos

The authors try to introduce and give a survey of two types of solution algorithms: the BONUS (Better Optimization of Nonlinear Uncertain System) algorithm and the L-shaped BONUS algorithm. the text is written in an understandable way and it should prove useful to specialists from different fields of investigation. (Vlasta Kakovį, Mathematical Reviews, May, 2016)

1 Introduction
1(8)
1.1 Stochastic Optimization Problems
1(3)
1.2 Stochastic Nonlinear Programming
4(2)
1.3 Summary
6(3)
Notations
7(2)
2 Uncertainty Analysis and Sampling Techniques
9(18)
2.1 Specifying Uncertainty Using Probability Distributions
9(1)
2.2 Sampling Techniques
10(3)
2.2.1 Monte Carlo Sampling
11(2)
2.3 Variance Reduction Techniques
13(11)
2.3.1 Importance Sampling
13(3)
2.3.2 Stratified Sampling
16(3)
2.3.3 Quasi-Monte Carlo Methods
19(5)
2.4 Summary
24(3)
Notations
24(3)
3 Probability Density Functions and Kernel Density Estimation
27(8)
3.1 The Histogram
27(1)
3.2 Kernel Density Estimator
28(4)
3.3 Summary
32(3)
Notations
34(1)
4 The Bonus Algorithm
35(22)
4.1 Reweighting Schemes
36(2)
4.2 Effect of Sampling on Reweighting
38(3)
4.3 Bonus: The Novel SNLP Algorithm
41(13)
4.4 Summary
54(3)
Notations
55(2)
5 Water Management Under Weather Uncertainty
57(10)
5.1 Introduction
57(1)
5.2 The Pulverized Coal Power Plant
57(3)
5.3 Parameter Uncertainty
60(1)
5.4 Problem Formulation
61(1)
5.5 Selection of Decision Variables
62(1)
5.6 Implementation of Bonus Algorithm
63(1)
5.7 Results
64(1)
5.8 Summary
65(2)
Notations
65(2)
6 Real-Time Optimization for Water Management
67(14)
6.1 Introduction
67(1)
6.2 Power Plant Operations
67(3)
6.3 Formulation of the Stochastic Problem
70(1)
6.4 Solution Approach
70(2)
6.5 Weather Forecasting and Uncertainty Quantification
72(3)
6.5.1 Ensemble Initialization
72(1)
6.5.2 Ensemble Propagation
73(1)
6.5.3 Validation of Weather Forecast
74(1)
6.6 Application to Pulverized Coal Power Plant
75(3)
6.7 Summary
78(3)
Notations
79(2)
7 Sensor Placement Under Uncertainty for Power Plants
81(14)
7.1 Introduction
81(3)
7.1.1 The Integrated Gasification Combined Cycle Power Plant
81(2)
7.1.2 Measurement Uncertainty
83(1)
7.2 Fisher Information and Its Use in the Sensor-Placement Problem
84(1)
7.3 Computation of Fisher Information
84(3)
7.3.1 Reweighting Using the Bonus Method
85(1)
7.3.2 Calculating the Fisher Information from Kernel Density Estimation
86(1)
7.4 The Optimization Problem
87(6)
7.4.1 Defining the Objective Function
87(1)
7.4.2 The IGCC Power Plant
88(2)
7.4.3 Problem Approach
90(1)
7.4.4 Results
91(2)
7.5 Summary
93(2)
Notations
93(2)
8 The L-Shaped Bonus Algorithm
95(22)
8.1 The L-Shaped Bonus Algorithm
100(2)
8.2 Illustrative Example 1: The Farmer's Problem
102(6)
8.2.1 Problem Formulation
102(3)
8.2.2 Problem Solution
105(2)
8.2.3 Results of the Farmer's Problem
107(1)
8.3 Illustrative Example 2: The Blending Problem
108(4)
8.3.1 Problem Formulation
109(2)
8.3.2 Simulations and Results
111(1)
8.4 Summary
112(5)
Notations
113(4)
9 The Environmental Trading Problem
117(10)
9.1 Introduction
117(1)
9.2 Basics of Pollutant Trading
117(1)
9.3 Christina Watershed Nutrient Management
118(1)
9.4 Trading Problem Formulation
119(5)
9.5 Results
124(2)
9.6 Summary
126(1)
Notations
126(1)
10 Water Security Networks
127(12)
10.1 Introduction
127(1)
10.2 Motivation and Prior Work
128(2)
10.3 Solution Methodology
130(3)
10.3.1 Use of Bonus Reweighting for Pattern Estimation
131(1)
10.3.2 Back Estimation of Flow Patterns
132(1)
10.4 Results
133(3)
10.5 Summary
136(3)
Notations
137(2)
References 139(4)
Index 143