Two methods for minimizing convex functions in a class of nonconvex sets
Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 48 (2008) no. 10, pp. 1802-1811
The conditional gradient method and the steepest descent method, which are conventionally used for solving convex programming problems, are extended to the case where the feasible set is the set-theoretic difference between a convex set and the union of several convex sets. Iterative algorithms are proposed, and their convergence is examined.
@article{ZVMMF_2008_48_10_a3,
author = {Yu. A. Chernyaev},
title = {Two methods for minimizing convex functions in a~class of nonconvex sets},
journal = {\v{Z}urnal vy\v{c}islitelʹnoj matematiki i matemati\v{c}eskoj fiziki},
pages = {1802--1811},
year = {2008},
volume = {48},
number = {10},
language = {ru},
url = {http://geodesic.mathdoc.fr/item/ZVMMF_2008_48_10_a3/}
}
TY - JOUR AU - Yu. A. Chernyaev TI - Two methods for minimizing convex functions in a class of nonconvex sets JO - Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki PY - 2008 SP - 1802 EP - 1811 VL - 48 IS - 10 UR - http://geodesic.mathdoc.fr/item/ZVMMF_2008_48_10_a3/ LA - ru ID - ZVMMF_2008_48_10_a3 ER -
Yu. A. Chernyaev. Two methods for minimizing convex functions in a class of nonconvex sets. Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 48 (2008) no. 10, pp. 1802-1811. http://geodesic.mathdoc.fr/item/ZVMMF_2008_48_10_a3/
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