Convolutional optimization with convex kernel and power lift
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916664717606912 |
|---|---|
| author | Lu, Zhipeng |
| author_facet | Lu, Zhipeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22135 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convolutional optimization with convex kernel and power lift Lu, Zhipeng Optimization and Control Computational Engineering, Finance, and Science Computation and Language 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. |
| title | Convolutional optimization with convex kernel and power lift |
| topic | Optimization and Control Computational Engineering, Finance, and Science Computation and Language |
| url | https://arxiv.org/abs/2503.22135 |