Kindly note that we have put a lot of effort into researching the best books on Numerical Optimization subject and came out with a recommended list of top 10 best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on "Numerical Optimization" as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.
|1. “Numerical Optimization With Applications” by Jayadeva and Suresh Chandra
“Numerical Optimization With Applications” Book Review: This book provides a detailed study of a variety of numerical optimization methods. It also discusses their applications in Science, Engineering and Management. This book covers standard optimization methods and their traditional applications. It offers updated topics like Semidefinite Programming, Second Order Cone Programming, Evolutionary Methods and Global optimization. It provides modern and non-conventional applications of numerical optimization. These are provided in the areas of Machine Learning, VLSI Design/ Electrical Circuits and Financial Mathematics.
|2. “Numerical Methods and Optimization” by Hari Arora|
|3. “Numerical Optimization (Springer Series in Operations Research and Financial Engineering)” by Jorge Nocedal and Stephen Wright|
|4. “Numerical Methods and Optimization Techniques for UoM (IV-Electrical-2012 course)” by DR J S CHITODE|
| 5. “Numerical Optimization: Theoretical and Practical Aspects (Universitext)” by Joseph-Frédéric Bonnans and Jean Charles Gilbert
“Numerical Optimization: Theoretical and Practical Aspects (Universitext)” Book Review: This book provides demonstrations of the ever present character of optimization. It explains numerical algorithms in a tutorial way. This book offers fundamental algorithms. It discusses more specialized and advanced topics for unconstrained and constrained problems. It consists of computational exercises in the form of case studies. This lets us understand optimization methods beyond their theoretical description.
|6. “Large Sparse Numerical Optimization (Lecture Notes in Computer Science)” by T F Coleman|
| 7. “Numerical Methods for Scientists and Engineers (Dover Books on Mathematics)” by Richard W Hamming
“Numerical Methods for Scientists and Engineers (Dover Books on Mathematics)” Book Review: This book ensures there is an intimate connection between the source of the problem and the usability of the answers in computing. It avoids isolated formulas and algorithms in favor of a systematic study of alternate ways of doing the problem. This book presents the evasion of roundoff. It helps us in overcoming the problem of truncation error. This book ensures the stability of a feedback system. It provides an emphasis on the frequency approach and its use in the solution of problems.
| 8. “Numerical Optimization and Swarm Intelligence for Optimization” by El-Shorbagy Mohammed A and El-Sawy Ahmed A
“Numerical Optimization and Swarm Intelligence for Optimization” Book Review: In this book swarm intelligence is applied with numerical optimization techniques. These techniques are to solve multiobjective engineering problems. This book presents a hybrid algorithm that combines both of the trust region algorithms. It also provides particle swarm optimization to solve multiobjective optimization problems. This book covers the new algorithm implemented to solve multiobjective engineering component design problems. This new algorithm is also used to solve the environmental economic dispatch problem.
| 9. “Complexity In Numerical Optimization” by Panos M Pardalos
“Complexity In Numerical Optimization” Book Review: This book provides articles on recent complexity developments in numerical optimization. It covers complexity of approximation algorithms, new polynomial time algorithms for convex quadratic minimization. This book includes interior point algorithms, complexity issues regarding test generation of NP-hard problems. It also discusses complexity of scheduling problems, min-max, fractional combinatorial optimization. This study offers fixed point computations and network flow problems. It discusses a broad spectrum of the direction in which research is going.
| 10. “Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (Applied Optimization)” by Klaus Schittkowski
“Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (Applied Optimization)” Book Review: This book provides a real life phenomena in engineering, natural, or medical sciences. This phenomena is presented by using a mathematical model with the goal to analyze numerically the behaviour of the system. This study provides information about the advantages of mathematical models. It serves to verify decisions, to avoid expensive and time consuming experimental tests. This text analyzes, understands, and explains the behaviour of systems, or to optimize design and production. It compares measured data with predicted model function values and to minimize the differences over the whole parameter space.
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