Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.00436 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915087176957952 |
|---|---|
| author | Seok, Jinwuk Cho, Changsik |
| author_facet | Seok, Jinwuk Cho, Changsik |
| contents | In this paper, we present an intuitive analysis of the optimization technique based on the quantization of an objective function. Quantization of an objective function is an effective optimization methodology that decreases the measure of a level set containing several saddle points and local minima and finds the optimal point at the limit level set. To investigate the dynamics of quantization-based optimization, we derive an overdamped Langevin dynamics model from an intuitive analysis to minimize the level set by iterative quantization. We claim that quantization-based optimization involves the quantities of thermodynamical and quantum mechanical optimization as the core methodologies of global optimization. Furthermore, on the basis of the proposed SDE, we provide thermodynamic and quantum mechanical analysis with Witten-Laplacian. The simulation results with the benchmark functions, which compare the performance of the nonlinear optimization, demonstrate the validity of the quantization-based optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00436 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intuitive Analysis of the Quantization-based Optimization: From Stochastic and Quantum Mechanical Perspective Seok, Jinwuk Cho, Changsik Machine Learning Quantum Physics In this paper, we present an intuitive analysis of the optimization technique based on the quantization of an objective function. Quantization of an objective function is an effective optimization methodology that decreases the measure of a level set containing several saddle points and local minima and finds the optimal point at the limit level set. To investigate the dynamics of quantization-based optimization, we derive an overdamped Langevin dynamics model from an intuitive analysis to minimize the level set by iterative quantization. We claim that quantization-based optimization involves the quantities of thermodynamical and quantum mechanical optimization as the core methodologies of global optimization. Furthermore, on the basis of the proposed SDE, we provide thermodynamic and quantum mechanical analysis with Witten-Laplacian. The simulation results with the benchmark functions, which compare the performance of the nonlinear optimization, demonstrate the validity of the quantization-based optimization. |
| title | Intuitive Analysis of the Quantization-based Optimization: From Stochastic and Quantum Mechanical Perspective |
| topic | Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2501.00436 |