A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations

This article provides an introduction to nonlinear constrained optimization using piecewise linear approximations, a technique that enables the use of linear programming (LP) and mixed-integer programming (MIP) solvers like Gurobi to handle complex nonlinear models. By approximating nonlinear functions with piecewise linear functions, practitioners can leverage the efficiency and scalability of LP/MIP solvers to solve optimization problems that would otherwise be difficult to tackle.

⚡ Key Takeaways

• Piecewise linear approximations can be used to handle nonlinear constrained models using LP/MIP solvers.
• This technique enables the solution of complex optimization problems that would be difficult to solve using traditional nonlinear programming methods.
• LP/MIP solvers like Gurobi can be used to solve optimization problems formulated using piecewise linear approximations.

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