As you know, Pyomo acts as a bridge between the problem formulation and the solvers. It provides a convenient and intuitive modeling language for users to define their optimization problems using mathematical equations, variables, constraints, and objectives. Once the problem is formulated in Pyomo, it is passed on to the solvers. Pyomo transforms the mathematical representation of the problem into a format that can be understood and solved by the chosen solver. The solver then applies various algorithms and techniques to search for the optimal solution.
Pyomo handles the communication between the user, who defines the problem, and the solvers, which perform the computational heavy-lifting to find the best solution. It provides a seamless interface to interact with different solvers, allowing users to easily switch between solvers or compare their performance for different types of optimization problems.
Solvers are essential tools in mathematical optimization that help us find optimal solutions to complex problems. Pyomo, a powerful optimization modeling language, requires solvers to solve mathematical programming models efficiently. Let’s explore some well-known open-source Pyomo solvers:
- GLPK: A versatile solver for linear programming (LP) and mixed-integer programming (MIP) problems. It is easy to use and integrates seamlessly with Pyomo, making it a popular choice for beginners.
- CBC: A reliable solver for mixed-integer linear programming (MILP) and MIP problems. It offers good performance and is actively maintained, ensuring ongoing improvements and bug fixes.
- IPOPT: An efficient solver for non-linear optimization problems. IPOPT handles large-scale problems effectively, supporting both continuous and integer variables. However, it requires additional installation steps compared to built-in solvers.
- Gurobi: A powerful commercial solver with excellent performance and comprehensive documentation. Gurobi offers an academic license option, making it a valuable choice for academic researchers and students.
Each solver has its strengths and considerations, so choose the one that suits your specific optimization requirements. Happy optimizing with Pyomo and these renowned solvers!
To install each solver after activating your environment using conda you can simply use the following example for glpk, CBC and ipopt:
conda install -c conda-forge glpk
conda install -c conda-forge coincbc
conda install -c conda-forge ipopt
Also to make sure that your solver is installed correctly you can check the solver’s version:
glpsol --version
cbc --version
ipopt --version