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Consider convex optimization problems subject to a large number of constraints.
Functional Operators
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The rate of convergence for the cyclic projections algorithm i: angles between convex sets
F. Deutsch and H. Hundal · 2006
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The rate of convergence for the cyclic projections algorithm ii: norms of nonlinear operators
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Relaxed alternating projection methods
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The rate of convergence for the cyclic projections algorithm iii: regularity of convex sets
F. Deutsch and H. Hundal · 2008
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Alternating projections on manifolds
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Incremental stochastic subgradient algorithms for convex optimization
S Sundhar Ram, Angelia Nedic, and Venugopal V Veeravalli · 2009
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Randomized methods for linear constraints: Convergence rates and conditioning
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Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
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Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization I: A generic algorithmic framework
S. Ghadimi and G. Lan · 2012
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A. Rakhlin, O. Shamir, and K. Sridharan · 2012
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Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization, II: Shrinking procedures and optimal algorithms
S. Ghadimi and G. Lan · 2013
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Random projection algorithms for convex set intersection problems
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Non-asymptotic analysis of stochastic approximation algorithms for machine learning
Eric Moulines and Francis R Bach · 2011
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A convergence theorem for nonnegative almost supermartingales and some applications
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
O. Shamir and T. Zhang · 2013
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Incremental constraint projection methods for variational inequalities
M. Wang and D.P. Bertsekas · 2014
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Incremental constraint projection-proximal methods for nonsmooth convex optimization
M. Wang and D.P. Bertsekas · 2014
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