The challenges faced by operational research (OR) practitioners, whether in operational scenarios or during techno-economic analyses, are typically intricate and inherently nonlinear. A skilled modeler's task is to simplify this complexity and identify the core issue whose resolution untangles the rest.
A common strategy is to linearize the problem to leverage the wealth of theoretical and practical tools provided by linear programming, such as duality, sensitivity analysis, and efficient algorithms. While this approach can be effective, one can often obtain significantly better solutions by directly solving a nonlinear model that more closely resembles the actual problem.
Artelys Knitro is the leading solver focused on large-scale, nonlinear (potentially non-convex), optimization problems. Knitro offers both interior-point and active-set algorithms for continuous models, as well as tools for handling problems with integer variables and other discrete structures. This tutorial will introduce the key features of Knitro, and demonstrate how to use Knitro to model and solve optimization problems in various environments.
This tutorial will showcase several instances of nonlinear problems, typically addressed through linear relaxation, yet warranting a direct approach. It will delve into the methodologies and tools employed to tackle these challenges. This tutorial aims at an audience familiar with the basics of mathematical optimization, focusing on practical examples.