{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/svgd-a-virtual-gradients-descent-method-for","title":"SVGD: A Virtual Gradients Descent Method for Stochastic Optimization","arxiv_id":"1907.04021","date":"2019-07-09","proceeding":null,"authors":["Zheng Li","Shi Shu"],"abstract":"Inspired by dynamic programming, we propose Stochastic Virtual Gradient Descent (SVGD) algorithm where the Virtual Gradient is defined by computational graph and automatic differentiation. The method is computationally efficient and has little memory requirements. We also analyze the theoretical convergence properties and implementation of the algorithm. Experimental results on multiple datasets and network models show that SVGD has advantages over other stochastic optimization methods.","url_abs":"https://arxiv.org/abs/1907.04021v2","url_pdf":"https://arxiv.org/pdf/1907.04021v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"svgd-a-virtual-gradients-descent-method-for","repo_url":"https://github.com/MindSpore-scientific-2/code-2/tree/main/SVGD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"svgd-a-virtual-gradients-descent-method-for","repo_url":"https://github.com/MindSpore-scientific/code-10/tree/main/SVGD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"svgd-a-virtual-gradients-descent-method-for","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/4/SVGD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"stochastic-optimization","task_name":"Stochastic Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}