Welcome to the Optimization Algorithms Repository. This repository provides a collection of Python implementations for a variety of optimization algorithms. These range from one-dimensional optimization methods to multidimensional techniques, constrained optimization, and dynamic programming solutions.

• 1D Optimization Algorithms
• Multidimensional Optimization Algorithms
• Constrained Optimization
• Dynamic Programming

• Description: Implements the bisection method for finding roots of a one-dimensional function.
• File: bisection.py

• Description: Implements the golden section search algorithm for locating the minimum of a unimodal function.
• File: golden_search.py

• Description: Implementation of the gradient descent algorithm for unconstrained optimization problems.
• File: gradient_descent.py

• Description: Implementation of Newton’s method for locating local minima or maxima in multidimensional spaces.
• File: newton.py

• Description: Implementation of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm for unconstrained optimization.
• File: bfgs.py

• Description: Implementation of the penalty function method for solving constrained optimization problems.
• File: penalty_method.py

• Description: Implementation of the barrier function method for constrained optimization.
• File: barrier_method.py

• Description: Dynamic programming solution to the coin change problem, determining the minimum number of coins required for a given amount.
• File: change_problem.py

• Description: Dynamic programming solution to the 0/1 knapsack problem.
• File: knapsack_problem.py