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Module 01: Optimization Foundations - Why Derivative-Free Methods Matter

Before evolving anything, understand what optimization is and why sometimes you cannot compute gradients. From noisy loss landscapes to black-box simulators, gradient descent cannot tread everywhere.

Learning Objectives​

  1. Distinguish gradient-based, gradient-free, and zeroth-order optimization
  2. Understand when derivative-free methods are necessary
  3. Implement random search and hill climbing from scratch
  4. Understand simulated annealing and its acceptance criterion
  5. Visualize fitness landscapes and understand local vs global optima

Concept Explanation​

Coming soon.

Code Examples​

Coming soon.

Exercises​

Coming soon.

Milestone Checklist​

  • Can explain when gradient-free methods are the right choice
  • Implemented random search and hill climbing
  • Visualized Rastrigin and Rosenbrock landscapes
  • Understand the No Free Lunch theorem

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