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First Course in Optimization

Charles Byrne
€72.99
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advanced undergraduate mathematics
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Author_Charles Byrne
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Auxiliary Functions
Basic Feasible Solution
Basics Of Continuous Optimization
Category1=Non-Fiction
Category=PBKS
Category=PBT
Category=PBW
Category=UB
Category=UY
Cauchy's Inequality
Cauchy’s Inequality
conjugate direction algorithms
Constrained And Unconstrained Optimization
convex analysis
Convex Set
COP=United Kingdom
CQ Algorithm
Delivery_Pre-order
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eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
exact and approximate solutions to systems of linear equations
geometric programming
GP
GP Problem
gradient methods
Induced Matrix Norm
Interior Point Methods
iterative optimization methods
iterative solution algorithms
KKT Theorem
Kl Distance
Language_English
Lim Inf
linear and convex programming
linear programming techniques
Linearly Independent
LU Factorization
mathematical optimization
matrix algebra
Newton?Raphson Algorithm
Non-basic Variable
Nonexpansive Operators
Nonnegative Solution
one-semester course in optimization
PA=Not yet available
Penalty Function Methods
Price_€50 to €100
PS=Forthcoming
QR Factorization
SFP
Single Real Variable
softlaunch
Starting Vector X0
Strong Duality Theorem
Support Theorem
vector spaces

Product details

  • ISBN 9781032922386
  • Weight: 580g
  • Dimensions: 156 x 234mm
  • Publication Date: 14 Oct 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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Description

Give Your Students the Proper Groundwork for Future Studies in Optimization

A First Course in Optimization is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering. It teaches students the basics of continuous optimization and helps them better understand the mathematics from previous courses.

The book focuses on general problems and the underlying theory. It introduces all the necessary mathematical tools and results. The text covers the fundamental problems of constrained and unconstrained optimization as well as linear and convex programming. It also presents basic iterative solution algorithms (such as gradient methods and the Newton–Raphson algorithm and its variants) and more general iterative optimization methods.

This text builds the foundation to understand continuous optimization. It prepares students to study advanced topics found in the author’s companion book, Iterative Optimization in Inverse Problems, including sequential unconstrained iterative optimization methods.

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