Practical Mathematical Optimization: Basic Optimization Theory and Gradient-Based Algorithms
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Guides readers to understand processes and strategies in real world optimization problems Contains new material on gradient-based methods, algorithm implementation via Python, and basic optimization principles Covers fundamental optimization concepts and definitions, search techniques for unconstrained minimization and standard methods for constrained optimization Includes example problems and exercises
Guides readers to understand processes and strategies in real world optimization problems Contains new material on gradient-based methods, algorithm implementation via Python, and basic optimization principles Covers fundamental optimization concepts and definitions, search techniques for unconstrained minimization and standard methods for constrained optimization Includes example problems and exercises
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