optimizn: a Python Library for Developing Customized Optimization Algorithms
This provides a tool for researchers and practitioners to more easily build optimization algorithms for domain-specific problems, but it is incremental as it builds on existing paradigms without introducing new methods.
The authors introduced optimizn, a Python library for developing customized optimization algorithms using paradigms like simulated annealing and branch and bound, enabling users to create tailored solutions for combinatorial problems with features like continuous training to improve results over time.
Combinatorial optimization problems are prevalent across a wide variety of domains. These problems are often nuanced, their optimal solutions might not be efficiently obtainable, and they may require lots of time and compute resources to solve (they are NP-hard). It follows that the best course of action for solving these problems is to use general optimization algorithm paradigms to quickly and easily develop algorithms that are customized to these problems and can produce good solutions in a reasonable amount of time. In this paper, we present optimizn, a Python library for developing customized optimization algorithms under general optimization algorithm paradigms (simulated annealing, branch and bound). Additionally, optimizn offers continuous training, with which users can run their algorithms on a regular cadence, retain the salient aspects of previous runs, and use them in subsequent runs to potentially produce solutions that get closer and closer to optimality. An earlier version of this paper was peer reviewed and published internally at Microsoft.