Course title: Optimization for Engineers
Lecture hours: 3
Typical Slot: B
Description:
- Frame a real world problem as a mathematical optimization problem.
- Identify the type of optimisation problem (continuous/discrete, linear/convex/non-convex).
- Apply standard optimisation algorithms to appropriate problems.
- Check a solution for optimality
Course content:
- Calculus basics: Gradients, linear approximations, quadratic approximation, Hessian, smoothness.
- Unconstrained optimisation: Gradient descent, Step-size rules, Newton’s method, non-linear equation solving.
- Linear programming: Linear constraints, feasibility, simplex method.
- Constrained optimization: Lagrangian, KKT conditions, projected gradient descent.
- Convexity: Convex sets and functions, local and global minima, convex optimisation problems.
Prerequisite: DA1001, DA1001
Books:
- Nocedal, Jorge and Wright, Stephen. Numerical optimization. Springer, 1999
- Luenberger, David G., and Yinyu Ye. Linear and nonlinear programming. 4th edition. Springer, 2015.
- Boyd, Stephen, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
Previous Instance of the course:
- 2025 (Jan-May; Dr. Gitakrishnan Ramadurai)