Theory of linear and integer programming
Theory of linear and integer programming
Integer and combinatorial optimization
Integer and combinatorial optimization
Approximate parametric searching
Information Processing Letters
Using separation algorithms in fixed dimension
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Linear Programming in Linear Time When the Dimension Is Fixed
Journal of the ACM (JACM)
Newton's method for fractional combinatorial optimization
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
Homology flows, cohomology cuts
Proceedings of the forty-first annual ACM symposium on Theory of computing
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We consider the problem of minimizing a convex function v(@l), over a closed convex set @L @? R^m, where for some fixed c @e @e R^n an n x m matrix A of reals, and a set X @? R^n, we define v(@l) = max . We show the following result: if for any given @l @e @L, and any fixed @e, 0 can also be determined by an @e-pproximation (strongly) polynomial combinatorial algorithm.