Recently completed training in optimization using Python, focusing on mathematical modeling and computational problem solving. A simple example from mathematics illustrates this well. Given 100 meters of fencing used to form three sides of a rectangular garden along a wall:2x + y = 100 Maximize the area: A = x · y Using calculus, the optimal solution is straightforward: x = 25, y = 50 → maximum area = 1250 However, the same problem can also be approached from an optimization perspective. Instead of solving analytically, the problem can be formulated by defining decision variables, constraints, and an objective function, and then solved computationally. The constraint is modeled as an equality (2x + y = 100), since the maximum area is achieved when all available fencing is fully used. While this is a simple example, the same approach extends directly to more complex problems in logistics, pricing, and resource allocation. It is a useful reminder that many real-world optimization problems are solved not through closed-form solutions, but through structured modeling and computational methods. #OperationsResearch #Optimization #Python #Mathematics #DataScience Here is a simple implementation in Python:
Good work!
It is nice to see you learning optimization 🥳