Optimization with Julia: Mastering Operations Research
Optimization with Julia: Mastering Operations Research
Solve optimization problems with Gurobi, CPLEX, GLPK, IPOPT, JuMP… using linear programming, nonlinear, MILP.
Created by Rafael Silva Pinto | 6 hours on-demand video course
The increasing complexity of the modern business environment has made operational and long-term planning for companies more challenging than ever. To address this, optimization algorithms are employed to find optimal solutions, and professionals skilled in this field are highly valued in today’s market. As an experienced data science team leader and holder of a PhD degree, I am well-equipped to teach you everything you need to solve optimization problems in both practical and academic settings.
In this Optimization with Julia: Mastering Operations Research course, you will learn how to problems problems using Mathematical Optimization, covering:
- Linear Programming (LP)
- Mixed-Integer Linear Programming (MILP)
- Nonlinear Programming (NLP)
- Mixed-Integer Nonlinear Programming (MINLP)
- Implementing summations and multiple constraints
- Working with solver parameters
- The following solvers: CPLEX, Gurobi, GLPK, CBC, IPOPT, Couenne, Bonmin, SCIP
This Optimization with Julia: Mastering Operations Research course is designed to teach you through practical examples, making it easier for you to learn and apply the concepts.
What you’ll learn
- Solve optimization problems using linear programming, mixed-integer linear programming, nonlinear programming, mixed-integer nonlinear programming
- Main solvers, including Gurobi, CPLEX, GLPK, CBC, IPOPT, Couenne, SCIP, Bonmin
- How to use JuMP to solve optimization problems with Julia
- How to solve problems with summations and multiple constraints
- How to install and use Julia
- How to install and activate each solver
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Optimization with Python: Solve Operations Research Problems
Pyomo Bootcamp: Python Optimization from Beginner to Advance
