Reformot

Reformot is a term used in optimization contexts to describe a framework for reformulating problems in order to improve tractability and solution quality. The name combines reformulation and optimization and is often used to refer to a family of methods rather than a single algorithm.

Origins and usage of reformot appear in late 2000s online discussions and subsequent academic abstracts as a generic label for techniques that transform challenging problems into more manageable forms. It is not a standardized protocol or a single software package, but rather a collection of approaches that share the goal of making difficult models easier to solve.

Core ideas of reformot involve reformulation strategies such as decoupling objectives and constraints, convexification, relaxation of difficult conditions, and surrogate modeling. The approach seeks to preserve the essential decision variables and problem structure while improving numerical properties and enabling more efficient solver performance.

Applications of reformot are cited across fields such as robotics path planning, industrial scheduling, supply chain optimization, and energy systems design. In robotics, reformot-inspired methods can yield smoother trajectories and plans that respect dynamic and safety constraints; in operations research, they help tackle large-scale problems by improving relaxation quality and solution time relative to direct formulations.

Limitations and evaluation: the effectiveness of reformot depends on the underlying problem structure and the quality of the reformulations. Poorly chosen transformations can degrade model fidelity or produce weak bounds. Most assessments compare reformot approaches to direct formulations and established heuristics regarding solution quality and computation time.

Related concepts include reformulation, relaxation, and convexification.