OPTIMIZATION TECHNIQUES FOR ENGINEERS
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Follow on Instagram for UpdatesOPTIMIZATION TECHNIQUES FOR ENGINEERS Syllabus — Unit-wise Content
The official GTU curriculum for OPTIMIZATION TECHNIQUES FOR ENGINEERS (subject code ME01067011) as prescribed for AI · Semester 1 divides the course into 7 units, covering 56 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 (ME01067011)Unit 1 Introduction to optimization
- Optimal problem formulation
- Design variables
- Objective function
- Variable bounds
- Optimization algorithms
Unit 2 Nonlinear Programming I
- Single-Variable optimization algorithms
- Optimality criteria
- Exhaustive search method
- Bounding phase method
- Region-Elimination methods
- Interval halving method
- Program Name: Engineering Level: PG Branch: Applied Instrumentation Course / Subject Code : ME01067011 CCourse / Subject Name : OPTIMIZATION TECHNIQUES FOR ENGINEERS w.e.f. 2024-25 http://syllabus.gtu.ac.in/ Page no. 1 of 2 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
Unit 3 Nonlinear Programming II
- Multivariable optimization algorithms
- optimality criteria
- Direct search methods
- Random search methods
- random walk method
- Random jumping method
- univariate method
- Hooke and Jeeve’s method
- Powell’s conjugate direction method
- Rosen brock’s method
- Simplex method
- Indirect search (descent) method
- Cauchy’s (steepest descent) method
- Conjugate gradient (Fletcher-Reeves) method
- Newton’s method
- Fletcher-Powell algorithm
Unit 4 Nonlinear Programming III
- Multivariable constrained optimization algorithms
- Generalized reduced gradient method
- Gradient projection method
- Box-Complex algorithm
- Kuhn-Tucker conditions, and Penalty function methods
Unit 5 Linear Programming
- Formulation of LPP, Graphical method
- Simplex method
- The use of artificial variables
- Big-M method
- Sensitivity analysis and duality theory
Unit 6 Optimal Control
- Objective function
- Performance index
- Calculus of variations
- Variational approach to optimal control problem
- Define Hamiltonian
Unit 7 Non-traditional Optimization algorithms
- Genetic algorithms
- Working Principles
- Differences between GAS and traditional methods
- Similarities between GAS and traditional methods
- GAS for constrained optimization
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