This course introduces the principles of Optimization Techniques. It provides the concepts of various classical and modern methods for constrained and unconstrained problems in both single and multivariable domains. The mathematical modeling and the transformation of optimization problems are also discussed. Many methods of modern optimization techniques are explored. The students will investigate and implement solutions of some real problems as case studies.
PhD Program
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content serial | Description |
---|---|
1 | Optimality Principle, Performance Measures |
2 | Classification of Optimization Problems |
3 | Mathematical Modeling of Optimization problems |
4 | Transformation of Optimization Problems |
5 | Single and Multi variable optimization |
6 | Unconstrained Optimization |
7 | Constrained Optimization |
8 | Discrete Optimization |
9 | Robust Optimization |
10 | Dynamic Optimization |
11 | Stochastic Optimization methods |
12 | Nature inspired Optimization methods |
13 | Optimization Case Study-1 (Network Design) |
14 | Optimization Case Study-2 (Image - Video processing) |
15 | Optimization Case Study-3 (Big-data Analytics) |
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