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DTSTAMP:20250805T113918
DTSTART;TZID=America/Detroit:20260212T150000
DTEND;TZID=America/Detroit:20260212T170000
SUMMARY:Social / Informal Gathering:Hopwood Tea
DESCRIPTION:Enjoy coffee\, tea\, and refreshments in a beautiful\, book-filled space. Check out a book from the Hopwood library or engage with other readers and writers. All are welcome.
UID:136054-21877788@events.umich.edu
URL:https://events.umich.edu/event/136054
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Food,Literary Arts,Literature,The Helen Zell Writers' Program,Undergraduate Students,Well-being,Writing,Ann Arbor,Books,Creative Writing,English Language And Literature,Free,Graduate Students,Hopwood Program
LOCATION:Angell Hall - 1176 (Hopwood Room)
CONTACT:
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DTSTAMP:20260126T132912
DTSTART;TZID=America/Detroit:20260212T150000
DTEND;TZID=America/Detroit:20260212T160000
SUMMARY:Lecture / Discussion:IOE 899: Soroosh Shafiee
DESCRIPTION:We investigate the problem of certifying optimality for sparse generalized linear models (GLMs)\, where sparsity is enforced through a cardinality constraint. While Branch-and-Bound (BnB) frameworks can certify optimality using perspective relaxations\, existing methods for solving these relaxations are computationally intensive\, limiting their scalability. To address this challenge\, we propose a unified proximal first-order algorithmic framework that is both linearly convergent and computationally efficient. We first develop a general theory for composite optimization problems satisfying specific geometric regularity conditions. \n\nBy establishing a rigorous link between primal quadratic growth and dual quadratic decay\, we derive novel error bounds showing that the computable duality gap can serve as a tight proxy for the distance to the solution set. Leveraging this property\, we design a restart scheme that upgrades generic sublinear algorithms to achieve provable linear convergence for both primal and dual objectives. We then instantiate this framework for the perspective relaxation of sparse GLMs. \n\nWe prove that standard GLM loss functions and the implicit perspective regularizer satisfy the required geometric conditions. Furthermore\, we develop specialized algorithms to evaluate the non-smooth regularizer and its proximal operator exactly in log-linear time\, avoiding the high cost of generic conic solvers. Extensive experiments on synthetic and real-world datasets demonstrate that our approach leverages GPU acceleration to speed up dual bound computations by orders of magnitude\, significantly enhancing the capability of BnB to certify optimality for large-scale problems.
UID:144573-21895503@events.umich.edu
URL:https://events.umich.edu/event/144573
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:899 Seminar Series,Graduate,Graduate Students,Industrial And Operations Engineering,Michigan Engineering
LOCATION:Industrial and Operations Engineering Building - 1680
CONTACT:
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