Optimization

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A01=Rajesh Kumar Arora
algorithm
ant colony optimization
augmented Lagrange multiplier
Author_Rajesh Kumar Arora
Basic Feasible Solution
branch and bound
Category=UMB
Central Difference Formula
Command Window
conjugate gradient method
design
Direct Search Methods
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
function
genetic
genetic algorithms
Geometric Programming
Golden Section Method
Goto Step
gradient and stochastic methods
Hessian Matrix
integer programming problems
Interior Point Method
linear programming problems
matlab
MATLAB Code
MDO Problem
multidisciplinary design optimization
Multiobjective Optimization Problem
Nonbasic Variable
objective
Objective Function
Optimization Problem
Pareto Front
Pareto Optimal Front
particle
particle swarm optimization
Probabilistic Dynamic Programming
problem
PSO
PSO Algorithm
PSO Method
robot trajectory optimization problem
Schwefel's Function
Simplex Method
simulated annealing
solution techniques for optimization problems
SQP Method
swarm
Test Problem
unconstrained and constrained optimization
variables

Product details

  • ISBN 9781498721127
  • Weight: 840g
  • Dimensions: 156 x 234mm
  • Publication Date: 06 May 2015
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Choose the Correct Solution Method for Your Optimization Problem

Optimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs.

The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden–Fletcher–Goldfarb–Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible directions, genetic algorithms, particle swarm optimization (PSO), simulated annealing, ant colony optimization, and tabu search methods. The author shows how to solve non-convex multi-objective optimization problems using simple modifications of the basic PSO code. The book also introduces multidisciplinary design optimization (MDO) architectures—one of the first optimization books to do so—and develops software codes for the simplex method and affine-scaling interior point method for solving linear programming problems. In addition, it examines Gomory’s cutting plane method, the branch-and-bound method, and Balas’ algorithm for integer programming problems.

The author follows a step-by-step approach to developing the MATLAB® codes from the algorithms. He then applies the codes to solve both standard functions taken from the literature and real-world applications, including a complex trajectory design problem of a robot, a portfolio optimization problem, and a multi-objective shape optimization problem of a reentry body. This hands-on approach improves your understanding and confidence in handling different solution methods. The MATLAB codes are available on the book’s CRC Press web page.

Rajesh Kumar Arora is a senior engineer at the Indian Space Research Organization, where he has been working for more than two decades. He obtained his PhD in aerospace engineering from the Indian Institute of Science, Bangalore. His research interests include mission design, simulation of launch vehicle systems, and trajectory optimization.

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