开源工具包，助你快速定制高效优化算法
optimizn: a Python Library for Developing Customized Optimization Algorithms
- 基于模拟退火与分支定界法，支持灵活构建自定义优化算法
- 支持持续训练，可复用历史最优特征提升求解精度
- 适合需要快速迭代求解的科研与工程人员
组合优化问题广泛存在于多个领域，通常具有复杂性高、难以高效求解的特点（多为NP-hard）。为此，本文提出 optimizn，一个用于在通用优化范式（如模拟退火、分支定界）下快速开发定制化优化算法的Python库。该库支持连续训练机制，允许用户按周期运行算法，保留前次运行中的关键特征，并在后续迭代中利用，从而逐步逼近最优解。该工作的早期版本已在微软内部经过同行评审并发表。
原文摘要 · Abstract (English)
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.
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