{"url":"/method/dabmd","slug":"dabmd","name":"DABMD","full_name":"Distributed Any-Batch Mirror Descent","full_name_withheld":false,"description_markdown":"**Distributed Any-Batch Mirror Descent** (DABMD) is based on distributed Mirror Descent but uses a fixed per-round computing time to limit the waiting by fast nodes to receive information updates from slow nodes. DABMD is characterized by varying minibatch sizes across nodes. It is applicable to a broader range of problems compared with existing distributed online optimization methods such as those based on dual averaging, and it accommodates time-varying network topology.","description_state":"present","introduced_year":2020,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":null,"title":null,"url_on_a_paper_host":false},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Replicated Data Parallel","url":"/methods/category/replicated-data-parallel","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Data Parallel Methods","url":"/methods/category/data-parallel-methods","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Optimization","url":"/methods/category/optimization","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Distributed Methods","url":"/methods/category/distributed-methods","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":null,"title":"Distributed Online Optimization over a Heterogeneous Network","date":"2020-01-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"papers_shown":1,"tasks":[],"tasks_shown":0,"n_tasks":0,"usage_by_year":[{"year":"2020","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/dabmd"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}