Recommended book for Optimisation?

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SUMMARY

The forum discussion centers on recommended textbooks for optimization courses, specifically highlighting "Numerical Optimization" by Nocedal and Wright and "Convex Optimization" by Boyd as essential resources for numerical computation. Additionally, "The Mathematical Theory of Optimal Processes" by Pontryagin is noted for its comprehensive coverage of optimal control, despite its complexity. Enid Pinch's "Optimal Control and the Calculus of Variations" is also mentioned as a more accessible option. The discussion emphasizes the importance of these texts in understanding multi-variable optimization problems and optimal control theory.

PREREQUISITES
  • Understanding of multi-variable calculus
  • Familiarity with linear algebra concepts
  • Knowledge of optimization problem formulation
  • Basic grasp of numerical methods for optimization
NEXT STEPS
  • Study "Numerical Optimization" by Nocedal and Wright for numerical computation techniques
  • Explore "Convex Optimization" by Boyd for insights into convex functions and optimization
  • Review "The Mathematical Theory of Optimal Processes" by Pontryagin for advanced optimal control theory
  • Investigate free online course notes on optimization for supplementary learning
USEFUL FOR

Students and professionals in engineering, finance, and information technology who are seeking to deepen their understanding of optimization techniques and applications.

Sherry Darlin
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I'm looking to do a course on Optimisation, however there was no prescribed textbook and I'm a bit wary of doing a course without a textbook to reference. There was a generalised list given, of like 10 textbooks, but this is a bit too much, especially with 3 other subjects to do!

Here is the general outline, perhaps someone can recommend 1 - 2 books?

Overview: Optimization is the study of problems in which we wish to optimize (either
maximize or minimize) a function (usually of several variables) often subject to a
collection of restrictions on these variables.

The restrictions are known as constraints
and the function to be optimized is the objective function. Optimization problems are
widespread in the modelling of real world systems, and cover a very broad range of
applications.

Problems of engineering design (such as the design of electronic circuits
subject to a tolerancing and tuning provision), information technology (such as the
extraction of meaningful information from large databases and the classication of
data), nancial decision making and investment planning (such as the selection of
optimal investment portfolios), and transportation management and so on arise in
the form of a multi-variable optimization problem or an optimal control problem.

Introduction: What is an optimization problem? Areas of applications of optimization.
Modelling of real life optimization problems.

Multi-variable optimization. Formulation of multi-variable optimization problems; Struc-
ture of optimization problems: objective functions and constraints. Mathematical
background: multi-variable calculus and linear algebra; (strict) local and (strict)
global minimizers and maximizers; convex sets, convex and concave functions; global
extrema and uniqueness of solutions.

Optimality conditions: First and second order conditions for unconstrained prob-
lems; Lagrange multiplier conditions for equality constrained problems; Kuhn-Tucker
conditions for inequality constrained problems.

Numerical Methods for Unconstrained Problems: Steepest descent method,

Newton's method, Conjugate gradient methods.

Numerical Methods for Constrained Problems: Penalty Methods.

Optimal Control: What is an optimal control problem? Areas of applications of optimal
control. Mathematical background: ordinary differential equations and systems of
linear differential equations.

The Pontryagin maximum principle: Autonomous control problems; unbounded
controls
 
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I definitely agree with Nick Alger with regards to Nocedal and Wright. It is a fantastic book for doing numerical compution: very clear, well motivated and explained, and very comprehensive.

On the other hand, neither of the above suggested books covers optimal control or Pontryagin's principle. Perhaps the most obvious reference here is Pontryagin's The Mathematical Theory of Optimal Processes. It is not exactly user friendly though, but is great if you need a reference for some examples or the actual proof of the PMP. Enid Pinch has a book Optimal Control and the Calculus of Variations which isn't too bad. You can find free course notes online (see attached). Finally, while unlikely, there is (in my opinion) the ultimate control theory book of all time, Jurdjevic's book Geometric Control Theory, though this might be too advanced.
 

Attachments

i am self learning physics. have you ever worked your way backwards again after finishing most undergrad courses? i have textbooks for junior/senior physics courses in classical mechanics, electrodynamics, thermal physics, quantum mechanics, and mathematical methods for self learning. i have the Halliday Resnick sophomore book. working backwards, i checked out Conceptual Physics 11th edition by Hewitt and found this book very helpful. What i liked most was how stimulating the pictures...

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