Optimization /Linear-Programming

Trying to use different frameworks and solvers on different problems

According to some articles online

https://www.osti.gov/biblio/1104761

http://www.tdp.cat/issues11/tdp.a114a12.pdf

https://www.sciencedirect.com/science/article/pii/S2211467X21001401

https://www.quora.com/What-are-some-good-open-source-alternatives-to-CPLEX-linear-program-solver

List of Linear Program Solver:

1. Open Source (Starting from best to good):

• Coin-Or (Clp),
• GLPK (Free Software Foundation (FSF)),
• LP Solver (lpsolve).

2. Commercial (better than open sources):

• CPLEX (IBM ILOG CPLEX Optimizer performance benchmarks),
• Gurobi

Captured from: Udemy Optimization with Python Solve Operations Research Problems

Optimization Problem Types

Captured from: https://neos-guide.org/guide/types/

In the figure below we can see the difference between local and global minimum. Optimization algorithms aim to find the global optimum result. However, not all the solvers managed to reach the global optimum. Some solvers stuck on the local minimum or couldn’t find the global minimum in reasonable time and computational resources. Captured from: https://python.plainenglish.io/applied-optimization-in-python-using-pyomo-3e11c7d08cc3

In the table below, we can see the list of the solvers. In the last column, it is indicated whether the solver is global optima.