Convolutional optimization with convex kernel and power lift
This work addresses a foundational problem in optimization theory for researchers, but it appears incremental as it builds on existing concepts with limited practical validation.
The paper tackled the problem of finding global optima for arbitrary functions by introducing a novel optimization theory based on convolution with convex kernels, aiming to provide a deterministic alternative to statistical models, with limited preliminary numerical results to test algorithm efficiency.
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.