Optimization Techniques
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Follow on Instagram for UpdatesOptimization Techniques Syllabus — Unit-wise Content
The official GTU curriculum for Optimization Techniques (subject code ME01000171) as prescribed for E(SE · Semester 1 divides the course into 5 units, covering 39 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME01000171)Unit 1 Introduction to Optimization
- Historical development
- Engineering Applications of Optimization
- Statement of an Optimization Problem
- A brief overview on classification of optimization problems [1]
Unit 2 Single Variable Optimization Algorithms
- Optimality criteria
- Bracketing methods – Exhaustive Search Method
- Bounding Phase Method
- Region Elimination methods – Interval Halving Method
- Fibonacci Search Method
- Golden Section Search Method
- Point Estimation method – Successive Quadratic Estimation Method
- Gradient based methods -Newton Raphson Method
- Bisection Method
- Secant Method
- Cubic Search Method [2]
Unit 3 Multivariable Optimization Algorithms
- Optimality criteria
- Unidirectional search
- Direct Search methods – Box’s Evolutionary Optimization Method
- Simplex Search Method
- Hooke Jeeves Pattern Search Method
- Gradient based methods - Cauchy’s Method
- Newton’s Method
- Marquardt’s Method
- Conjugate Gradient Method [2] 09 Program Name: Engineering Level: PG Branch: Electrical Engineering Course / Subject Code: ME01000171 Course / Subject Name: Optimization Techniques
Unit 4 Constrained Optimization Algorithms
- Kuhn Tucker Conditions
- Lagrangian Duality Theory
- Transformation Methods - Penalty function Method
- Method of Multipliers
- Direct Search for Constrained Minimization – Variable Elimination Method
- Complex Search Method
- Random Search Methods
- Linearized Search Methods – Frank Wolfe Method
- Cutting Plane Method [2]
Unit 5 Modern Methods of Optimization- Introduction, Genetic Algorithms,
- Simulated Annealing
- Particle Swarm Optimization
- Ant Colony Optimization
- Optimization of Fuzzy Systems
- Neural Network based optimization
- Teaching Learning Based Optimization
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