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All Pages Books Journals. Authors: Ch. Venkateswarlu Jujjavarapu Satya Eswari. Paperback ISBN: Imprint: Elsevier. Published Date: 1st November Page Count: Pre-Order Expected Release date: November 1, Sorry, this product is currently out of stock.

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Outlines design, analysis and implementation of optimization strategies to solve complex optimization problems of different domains Highlights a number of real applications concerning chemical, biochemical, pharmaceutical and environmental engineering processes. Applications to Chemical Processes 6. Applications to Biochemical Processes 7.

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Applications to Pharmaceutical Processes 8. Applications to Environmental Processes 9. Powered by. You are connected as. Connect with:. In particular, the difference in the speed of the GPU when performing calculations with a single and double precision was considered. To ensure the efficiency of calculations based on optimization algorithms, it is recommended to carry out calculations with the use of single precision, and increase the calculation precision in case of impossibility to achieve the desired accuracy of the result.

Stochastic Optimization Methods in Finance and Energy

There is considering the significantly higher performance of graphics processors when doing calculations with a single precision in comparison with calculations with double precision it is expedient to use a single calculation precision when graphic processors are used to solve considered problem. Double precision can be used if it is difficult to get sufficiently correct solution by single precision calculations.

The results of numerical experiments confirm that the use of lower precision to perform optimization for macromodels creation has a slight influence on the speed of achieving of predetermined optimization accuracy. Key words: stochastic optimization methods, graphics processors, optimization speed. On the other hand, probabilistically robust optimization models [ 25 ] quantify the uncertainty in the real value of the parameter of interest using a probability distribution function.

Additional classifications are global robustness [ 24 ], or non-probabilistic robust optimization models [ 26 ]. As has been pointed along this introduction, uncertainty is present in lots of real-world situations. For this reason, robust optimization has also been frequently used for modeling a wide variety of real problems, belonging to different knowledge areas, such as supply chain network design [ 27 ] or food distribution [ 28 ].

This introductory chapter highlights the potential that Nature-Inspired solvers may bring to stochastic, robust, and dynamic optimization problems. Nature has learned from itself from the very beginning of Earth, with manifold processes and intelligent behaviors that have naturally evolved over ages to attain high levels of adaptability and efficiency. It is now time for researchers, lecturers, and practitioners interested in Nature-Inspired optimization to shift their target and span the application of this algorithmic branch to these optimization problems, far less studied so far by the community than other formulated optimization problems.

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Methods for Nonlinear and Stochastic Optimization | Argonne National Laboratory

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Downloaded: Introduction Optimization is one of the most studied fields in the wide field of artificial intelligence. Dynamic optimization In optimization problems, it is often the case that the parameters based on which fitness function s and constraints are defined remain unaltered over the period of time in which the solution obtained by the solver is considered to be optimal.

Stochastic optimization Stochastic optimization is another problem variant that finds its motivation in real application scenarios. Robust optimization The third class of optimization problems targeted by this chapter is robust optimization, which denotes a branch of problems where one or more variables that compose the problem is also subject to uncertainty.

Conclusions This introductory chapter highlights the potential that Nature-Inspired solvers may bring to stochastic, robust, and dynamic optimization problems. More Print chapter.