Optimization Techniques

Master mathematical optimization methods to solve complex problems and make better decisions in engineering, business, and data science

Explore Topics Explore Lectures

Why Learn Optimization?

Optimization techniques are essential tools for solving complex problems across various domains, from engineering to finance and artificial intelligence.

Improve Efficiency

Learn to maximize performance and minimize costs in any system or process.

AI & Machine Learning

Understand the optimization algorithms that power modern AI systems.

Engineering Applications

Apply optimization to design better products and systems with limited resources.

Decision Making

Develop frameworks for making optimal decisions in uncertain environments.

Course Topics

Comprehensive coverage of optimization methods with practical applications and case studies.

Linear Programming

Foundation of optimization techniques

  • Formulating linear programs
  • Simplex method
  • Duality theory
  • Sensitivity analysis
  • Applications in operations

Nonlinear Optimization

Solving complex real-world problems

  • Convexity and concavity
  • Karush-Kuhn-Tucker conditions
  • Gradient descent methods
  • Newton's method
  • Constrained optimization

Integer & Combinatorial

Discrete optimization techniques

  • Integer programming
  • Branch and bound
  • Cutting plane methods
  • Network optimization
  • Scheduling problems

Stochastic Optimization

Dealing with uncertainty

  • Markov decision processes
  • Dynamic programming
  • Stochastic programming
  • Robust optimization
  • Applications in finance

Metaheuristics

Advanced optimization methods

  • Genetic algorithms
  • Simulated annealing
  • Tabu search
  • Particle swarm optimization
  • Ant colony optimization

Multi-objective Optimization

Balancing competing goals

  • Pareto optimality
  • Weighted sum method
  • ε-constraint method
  • Evolutionary approaches
  • Decision support

Course Details

Everything you need to know before enrolling in this optimization course

Prerequisites

  • Basic linear algebra (vectors, matrices)
  • Calculus (derivatives, gradients)
  • Fundamental probability concepts
  • Programming experience (Python recommended)
  • Mathematical maturity (proofs helpful but not required)

Don't have all prerequisites? Preparatory materials will be provided.

Recommended Textbooks

Primary Textbook:

"Introduction to Linear Optimization" by Bertsimas and Tsitsiklis

Supplementary:

"Numerical Optimization" by Nocedal and Wright

"Nonlinear Programming" by Dimitri Bertsekas

"Algorithms for Optimization" by Kochenderfer and Wheeler

Comprehensive notes provided so no need to worry !!.

Course Format

Video Lectures

12 weekly modules with 3-5 video lectures each (15-20 mins per video)

Hands-on Exercises

Weekly programming assignments with real-world datasets

MATLAB codes

MATLAB scripts are included for implementation.