NDLI: Playing Non-linear Games with Linear Oracles

Content Provider	IEEE Xplore Digital Library
Author	Garber, D. Hazan, E.
Copyright Year	2013
Description	Author affiliation: Technion - Israel Inst. of Technol., Haifa, Israel (Garber, D.; Hazan, E.)
Abstract	Linear optimization is many times algorithmically simpler than non-linear convex optimization. Linear optimization over matroid polytopes, matching polytopes and path polytopes are example of problems for which we have efficient combinatorial algorithms, but whose non-linear convex counterpart is harder and admit significantly less efficient algorithms. This motivates the computational model of online decision making and optimization using a linear optimization oracle. In this computational model we give the first efficient decision making algorithm with optimal regret guarantees, answering an open question of Kalai and Vempala, Hazan and Kale, in case the decision set is a polytope. We also give an extension of the algorithm for the partial information setting, i.e. the "bandit" model. Our method is based on a novel variant of the conditional gradient method, or Frank-Wolfe algorithm, that reduces the task of minimizing a smooth convex function over a domain to that of minimizing a linear objective. Whereas previous variants of this method give rise to approximation algorithms, we give such algorithm that converges exponentially faster and thus runs in polynomial-time for a large class of convex optimization problems over polyhedral sets, a result of independent interest.
Starting Page	420
Ending Page	428
File Size	254748
Page Count	9
File Format	PDF
ISBN	9780769551357
ISSN	02725428
DOI	10.1109/FOCS.2013.52
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Games Convex functions Approximation algorithms Convergence Gradient methods Algorithm design and analysis Convex Optimization Online Algorithms Regret Minimization
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Playing games with approximation algorithms

Playing Games with Approximation Algorithms

Rate of convergence analysis of a dual fast gradient method for general convex optimization

On Convergence Analysis of Iterative Smoothing Methods for a Class of Nonsmooth Convex Minimization Problems

On the convergence time of the drift-plus-penalty algorithm for strongly convex programs

Global convergence of the Heavy-ball method for convex optimization

On the convergence rate of a Distributed Augmented Lagrangian optimization algorithm

Beyond the regret minimization barrier: an optimal algorithm for stochastic stronglyconvex optimization (2011)

Efficient algorithms for online game playing and universal portfolio management (2005)

Playing Non-linear Games with Linear Oracles

Similar Documents

Playing games with approximation algorithms

Playing Games with Approximation Algorithms

Rate of convergence analysis of a dual fast gradient method for general convex optimization

On Convergence Analysis of Iterative Smoothing Methods for a Class of Nonsmooth Convex Minimization Problems

On the convergence time of the drift-plus-penalty algorithm for strongly convex programs

Global convergence of the Heavy-ball method for convex optimization

On the convergence rate of a Distributed Augmented Lagrangian optimization algorithm

Beyond the regret minimization barrier: an optimal algorithm for stochastic stronglyconvex optimization (2011)

Efficient algorithms for online game playing and universal portfolio management (2005)

Playing Non-linear Games with Linear Oracles