Optimization with Python: Solve Operations Research Problems
Optimization with Python: Solve Operations Research Problems Course
Solve optimization problems with CPLEX, Gurobi, Pyomo. using linear programming, nonlinear, evolutionary algorithms.
Operational planning and long-term planning for companies are more complex in recent years. Information changes fast, and the decision making is a hard task. Therefore, optimization algorithms (operations research) are used to find optimal solutions for these problems. Professionals in this field are one of the most valued in the market. In this Optimization with Python: Solve Operations Research Problems Course you will learn what is necessary to solve problems applying Mathematical Optimization and Metaheuristics:
- Linear Programming (LP)
- Mixed-Integer Linear Programming (MILP)
- NonLinear Programming (NLP)
- Mixed-Integer Linear Programming (MINLP)
- Genetic Algorithm (GA)
- Multi-Objective Optimization Problems with NSGA-II (an introduction)
- Particle Swarm (PSO)
- Constraint Programming (CP)
- Second-Order Cone Programming (SCOP)
- NonConvex Quadratic Programming (QP)
The classes use examples that are created step by step, so we will create the algorithms together. Besides this Optimization with Python: Solve Operations Research Problems Course is more focused in mathematical approaches, you will also learn how to solve problems using artificial intelligence (AI), genetic algorithm, and particle swarm.
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What you’ll learn
- Solve optimization problems using linear programming, mixed-integer linear programming, nonlinear programming, mixed-integer nonlinear programming,
- LP, MILP, NLP, MINLP, SCOP, NonCovex Problems
- Main solvers and frameworks, including CPLEX, Gurobi, and Pyomo
- Genetic algorithm, particle swarm, and constraint programming
- From the basic to advanced tools, learn how to install Python and how to use the main packages (Numpy, Pandas, Matplotlib…)
- How to solve problems with arrays and summations
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