{"id":205,"date":"2024-07-14T19:51:00","date_gmt":"2024-07-14T19:51:00","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/annual2025\/?page_id=205"},"modified":"2025-10-02T15:30:40","modified_gmt":"2025-10-02T20:30:40","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/annual2025\/tutorials\/","title":{"rendered":"TutORials"},"content":{"rendered":"\n
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The TutORials in Operations Research<\/em> series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new developments in the field and serves as augmenting material for a selection of the tutorial presentations offered at the INFORMS Annual Meeting.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Large-Scale Optimization via Monotone Operators<\/h3>\n
This tutorial presents a unified analysis of convex optimization algorithms through<\/div>\n
the abstraction of monotone operators. Through this streamlined approach, we derive<\/div>\n
and analyze a wide variety of classical and modern algorithms, including: Gradient<\/div>\n
Descent, Dual Ascent, Proximal Point Method, Proximal Gradient Method, Projected<\/div>\n
Gradient Method, Forward-Backward Splitting, Peaceman\u2013Rachford Splitting,<\/div>\n
Douglas\u2013Rachford Splitting, Davis\u2013Yin Splitting, Method of Multipliers, Proximal<\/div>\n
Method of Multipliers, Alternating Direction Method of Multipliers, Alternating Minimization Algorithm, Primal-dual hybrid gradient (PDHG), PDLP, and Condat\u2013V\u0169.<\/div>\n
\u00a0<\/div>\n

Speakers: Ernest Ryu and Wotao Yin<\/strong><\/p> <\/div>\n<\/div>\n\n

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