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Examples of applications are taken from a variety of domains (physics, chemistry, mechanics, mining, engineering, computer science, etc.) to give the reader a sense of how widely such applications can be used
A convenient one-stop reference combining viewpoints from the fields of both mathematics and computer science, with details on software and on applications
The final chapter on web resources points the reader to further material to continue the learning process
Initial training in pure and applied sciences tends to present problem-solving as the process of elaborating explicit closed-form solutions from basic principles, and then using these solutions in numerical applications. This approach is only applicable to very limited classes of problems that are simple enough for such closed-form solutions to exist. Unfortunately, most real-life problems are too complex to be amenable to this type of treatment. Numerical Methods and Optimization – A Consumer Guide presents methods for dealing with them.
Shifting the paradigm from formal calculus to numerical computation, the text makes it possible for the reader to
discover how to escape the dictatorship of those particular cases that are simple enough to receive a closed-form solution, and thus gain the ability to solve complex, real-life problems
understand the principles behind recognized algorithms used in state-of-the-art numerical software
learn the advantages and limitations of these algorithms, to facilitate the choice of which pre-existing bricks to assemble for solving a given problem and
acquire methods that allow a critical assessment of numerical results.
Numerical Methods and Optimization – A Consumer Guide will be of interest to engineers and researchers who solve problems numerically with computers or supervise people doing so, and to students of both engineering and applied mathematics.
Content Level » Research
Keywords » Linear Algebra - Numerical Algorithms - Optimization - Simulation - Systems of Equations
Related subjects » Computational Intelligence and Complexity - Computational Science &amp Engineering - Mathematics - Theoretical Computer Science