Description
Linear Optimization Problems with Inexact Data addresses the critical challenge of solving optimization problems when input data contains uncertainties, errors, or imprecision. Written by leading experts Miroslav Fiedler, Josef Nedoma, Jaroslav Ramik, Jiri Rohn, Karel Zimmermann and collaborators, this Springer publication bridges theory and practice.
The book systematically develops robust optimization techniques applicable to linear programming problems with interval coefficients, fuzzy parameters, and other forms of data uncertainty. It covers sensitivity analysis, stability regions, and computational methods for finding optimal solutions despite data imprecision.
Essential for researchers, practitioners, and students in operations research, applied mathematics, and engineering, this work provides rigorous mathematical foundations combined with practical algorithmic approaches. The comprehensive treatment includes real-world applications across economics, engineering, and management science.
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