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Line search (or backtracking) procedures have been widely employed into first-order methods for solving convex optimization problems, especially those with unknown problem parameters (e.g., Lipschitz constant).
Minimization of functions having Lipschitz continuous first partial derivatives
L. Armijo · 1966
Earlier work this paper cites.
Problem complexity and method efficiency in optimization
A. Nemirovski and D. Yudin · 1983
Earlier work this paper cites.
Information-based complexity of mathematical programming
A. Nemirovskii and D. Yudin · 1983
Earlier work this paper cites.
A method for unconstrained convex minimization problem with the rate of convergence O ( 1 / k 2 ) O(1/k^{2})
Y. Nesterov · 1983
Earlier work this paper cites.
Optimal methods of smooth convex minimization
A. S. Nemirovskii and Y. E. Nesterov · 1985
Earlier work this paper cites.
New variants of bundle methods
C. Lemaréchal, A. S. Nemirovski, and Y. E. Nesterov · 1995
Earlier work this paper cites.
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Earlier work this paper cites.
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