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
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 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 course is designed to teach you through practical examples, making it easier for you to learn and apply the concepts.
If you are new to Julia or programming in general, don't worry! I will guide you through everything you need to get started with optimization, from installing Julia and learning its basics to tackling complex optimization problems.
By completing this course, you'll not only enhance your skills but also earn a valuable certification from Udemy.
Operations Research | Operational Research | Operation Research | Mathematical Optimization
I look forward to seeing you in the classes and helping you advance your career in operations research!
