CVXPyomo

CVXPYomo is a Python package that bridges the gap between CVXPY, a popular convex optimization modeling language, and Pyomo, a comprehensive optimization modeling framework. It allows users to formulate and solve convex optimization problems using Pyomo's object-oriented modeling capabilities while leveraging CVXPY's robust convex optimization solvers. This integration enables the representation of complex optimization models with structured data and the efficient solution of problems that are guaranteed to have a global optimum due to their convexity.

The primary functionality of CVXPYomo is to translate Pyomo models into a format that CVXPY can understand and process. This involves mapping Pyomo's variables, parameters, objectives, and constraints to their corresponding CVXPY representations. Convexity is a key requirement; CVXPYomo can only handle models that are mathematically convex. If a model is not convex, CVXPY will typically report an error or may not find a guaranteed global solution.

By combining the strengths of both libraries, CVXPYomo offers a powerful tool for researchers and practitioners. Pyomo's extensive modeling features, including its support for discrete variables and complex data structures, are retained, while CVXPY provides access to a wide array of efficient convex solvers. This makes CVXPYomo particularly useful for applications in areas such as machine learning, operations research, and control systems where convex optimization plays a significant role.