Geodesic
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

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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. 
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     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},
     publisher = {mathdoc},
     volume = {48},
     number = {10},
     year = {2008},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ZVMMF_2008_48_10_a3/}
}
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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/