[Ml-stat-talks] FW: TODAY at 4:30PM: Jonathan Eckstein, Optimization seminar

Amir Ali Ahmadi a_a_a at princeton.edu
Thu Jan 15 10:39:48 EST 2015

Of possible interest...

From: Optimization Seminar [opt-seminar at Princeton.EDU] on behalf of Carol Smith [carols at PRINCETON.EDU]
Sent: Thursday, January 15, 2015 10:08 AM
To: opt-seminar at Princeton.EDU
Subject: TODAY at 4:30PM: Jonathan Eckstein, Optimization seminar

-----   Princeton Optimization Seminar   -----

DATE:  Thursday, January 15, 2014

TIME:  4:30pm

LOCATION:  Sherrerd Hall room 101

SPEAKER:  Jonathan Eckstein, Rutgers University

TITLE:  Approximate Versions of the Alternating Direction Method of Multipliers
The Alternating Direction Method of Multipliers (ADMM) is a
decomposition method for convex optimization, currently enjoying some
popularity in the solution of machine learning, image processing, and
stochastic programming problems. This talk reviews the convergence
mechanism of the ADMM and presents three new, provably convergent
approximate versions in which one or (in two variants) both
optimization subproblems arising at each iteration may be solved
inexactly using practically testable approximation criteria.
Preliminary computational results applying two of these methods to two
different problem classes indicate that in cases in which an iterative
method is used for at least one of the ADMM subproblems, these
variants can significantly reduce total computational effort. We also
discuss the application of these approximation techniques to the
Progressive Hedging (PH) algorithm for stochastic programming, which
may be viewed as an particular case of the ADMM.
Joint work with Wang Yao (doctoral candidate), Rutgers University.
Jonathan Eckstein is a Professor in the department of Management
Science and Information Systems at Rutgers University. His principle
research interests are in numerical optimization algorithms, both
continuous and discrete, and especially their implementation on
parallel computing platforms. Areas of particular focus include
augmented Lagrangian/proximal methods, branch-and-bound algorithms,
and stochastic programming. He has also worked on risk-averse
optimization modeling and on applying O.R. techniques to managing
information systems. He completed his Ph.D. in Operations Research at
M.I.T. in 1989, and then taught at Harvard Business School for two
years. He then spent four years in the Mathematical Sciences Research
Group of Thinking Machines, Inc. before joining Rutgers. At Rutgers,
he led an effort establishing a new undergraduate major in Business
Analytics and Information Technology ("BAIT"). In 2014, he was
elected a fellow of INFORMS (the Institute for Operations Research and
Management Science).
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