Constructive duality for discrete optimization

Later, it was briefly mentioned in [10] that the meet-in-the-middle trick can be used to solve the discrete logarithm problem if votes are packed homomorphicly using the

To understand the convergence mechanism of incremental gradient methods, let us consider the case X = < n , and assume that the component functions f i are selected for

The worst-case complexity bounds for the problem (1) with the exact and stochastic first order oracle are available for the case of strongly convex objective (see, e.g... [18, 1]

The original Karp and Miller algorithm ( K&M ) unfolds the reachability graph of a Petri net and uses acceleration on branches to ensure termination.. The MP algorithm improves

[16] introduced another iterative scheme by the viscosity approximate method for finding a common element of the set of solutions of a varia- tional inclusion with set-valued

In this paper, we summarize tests using different moduli on the same text, tests run on different English texts (fiction, scientific, law, newspaper), and tests run on

Our signature method is a variant of Balinski's [3] compétitive (dual) simplex algorithm. It constructs rooted trees that have precisely the same primai structure as Balinski's

This is done through convex duality and implies notably that for certain problems, such as for supervised machine learning problems with nonsmooth losses or problems regularized