用凸核卷积构建新优化理论，找任意函数全局最优解
Convolutional optimization with convex kernel and power lift
- 基于凸核卷积设计确定性优化框架
- 初步实验验证特定算法有效，可定位全局最优
- 适合追求理论严谨性的优化研究者
我们致力于建立一种基于凸核卷积的新型优化理论基础。目标是构建一个道德上确定性的模型，用于定位任意函数的全局最优解，区别于大多数常用的统计模型。提供了有限的初步数值结果，以测试从该范式推导出的一些特定算法的效率，期望能激发进一步的实际兴趣。
原文摘要 · Abstract (English)
We focus on establishing the foundational paradigm of a novel optimization theory based on convolution with convex kernels. Our goal is to devise a morally deterministic model of locating the global optima of an arbitrary function, which is distinguished from most commonly used statistical models. Limited preliminary numerical results are provided to test the efficiency of some specific algorithms derived from our paradigm, which we hope to stimulate further practical interest.
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