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In this paper, we propose and analyze zeroth-order stochastic approximation algorithms for nonconvex and convex optimization, with a focus on addressing constrained optimization, high-dimensional setting and saddle-point avoiding.
An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe · 1956
Earlier work this paper cites.
Approximate methods in optimization problems
V. Demyanov and A. Rubinov · 1970
Earlier work this paper cites.
A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Charles Stein · 1972
Earlier work this paper cites.
Estimation of the mean of a multivariate normal distribution
Charles M Stein · 1981
Earlier work this paper cites.
The gap function of a convex program
Donald Hearn · 1982
Earlier work this paper cites.
Problem complexity and method efficiency in optimization
A. S. Nemirovski and D. Yudin · 1983
Earlier work this paper cites.
Some np-complete problems in quadratic and nonlinear programming
Katta G Murty and Santosh N Kabadi · 1987
Earlier work this paper cites.
Optimum bounds for the distributions of martingales in banach spaces
Iosif Pinelis · 1994
Earlier work this paper cites.
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Ahron Ben-Tal and Arkadi Nemirovski · 2001
Earlier work this paper cites.
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Stephen Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
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Y. E. Nesterov · 2004
Earlier work this paper cites.
Introduction to stochastic search and optimization: estimation, simulation, and control
James Spall · 2005
Earlier work this paper cites.
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Earlier work this paper cites.
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Jorge Nocedal and Stephen J Wright · 2006
Earlier work this paper cites.
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Adaptive cubic regularisation methods for unconstrained optimization. part i: motivation, convergence and numerical results
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Adaptive cubic regularisation methods for unconstrained optimization. part ii: worst-case function-and derivative-evaluation complexity
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Elad Hazan and Satyen Kale · 2012
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Kevin Jamieson, Robert Nowak, and Ben Recht · 2012
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