An algorithm for nonsmooth convex minimization with errors
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- by Krzysztof C. Kiwiel PDF
- Math. Comp. 45 (1985), 173-180 Request permission
Abstract:
A readily implementable algorithm is given for minimizing any convex, not necessarily differentiable, function f of several variables. At each iteration the method requires only one approximate evaluation of f and its $\varepsilon$-subgradient, and finds a search direction by solving a small quadratic programming problem. The algorithm generates a minimizing sequence of points, which converges to a solution whenever f has any minimizers.References
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Additional Information
- © Copyright 1985 American Mathematical Society
- Journal: Math. Comp. 45 (1985), 173-180
- MSC: Primary 90C25; Secondary 65K05
- DOI: https://doi.org/10.1090/S0025-5718-1985-0790650-5
- MathSciNet review: 790650