/Optimization

Implementing Optimizers from scratch

Primary LanguageJupyter Notebook

Learning and implementing optimizers from scratch.

  • Critical point analysis
  • Linear Regression with:
    • Manual search
    • Grid search
    • Random search
  • Studying Gaussian Distribution
  • Implementing penalty functions:
    • Death
    • Static
  • Developing Genetic Algorithm from scratch for Knapsack problem
  • Capstone: Finding feasible solution for a given optimization problem
    • Under constraints
    • Using Penalty functions
    • Through Random search and Simulated Annealing
    • Statistical analysis of the results on 21 runs of each algorithm
    • Null-hypothesis statistical test