TL;DR: The Frobenius number for small n is a special case of the number of numbers in the general problem as mentioned in this paper, and it has been used in many applications in the literature.
Abstract: Preface Acknowledgements 1. Algorithmic Aspects 2. The Frobenius Number for Small n 3. The General Problem 4. Sylvester Denumerant 5. Integers without Representation 6. Generalizations and Related Problems 7. Numerical Semigroups 8. Applications of the Frobenius Number 9. Appendix A Bibliography
TL;DR: In this paper, it was shown that the Frobenius problem can be solved in polynomial time for integer semigroups and Hilbert bases of rational cones, provided certain parameters (the dimension and the number of generators) are specified.
Abstract: . We prove that for any fixed d the generating function of the projectionof the set of integer points in a rational d-dimensional polytope can be computed inpolynomial time. As a corollary, we deduce that various interesting sets of latticepoints, notably integer semigroups and (minimal) Hilbert bases of rational cones,have short rational generating functions provided certain parameters (the dimensionand the number of generators) are fixed. It follows then that many computationalproblems for such sets (for example, finding the number of positive integers notrepresentable as a non-negative integer combination of given coprime positive integersa 1 ,... ,a d ) admit polynomial time algorithms. We also discuss a related problem ofcomputing the Hilbert series of a ring generated by monomials. 1. Introduction and Main ResultsOur main motivation is the following question which goes back to Frobenius andSylvester.(1.1) The Frobenius Problem. Let a 1 ,... ,a d be positive coprime integers andletS =nµ
TL;DR: It is proved that in order to succeed in this model of read-once width-$w branching programs, $\beta$ must be at least $1/ (\log n)^{\Theta(w)}$.
Abstract: The \emph{Coin Problem} is the following problem: a coin is given, which lands on head with probability either $1/2 + \beta$ or $1/2 - \beta$. We are given the outcome of $n$ independent tosses of this coin, and the goal is to guess which way the coin is biased, and to answer correctly with probability $\ge 2/3$. When our computational model is unrestricted, the majority function is optimal, and succeeds when $\beta \ge c /\sqrt{n}$ for a large enough constant $c$. The coin problem is open and interesting in models that cannot compute the majority function. In this paper we study the coin problem in the model of \emph{read-once width-$w$ branching programs}. We prove that in order to succeed in this model, $\beta$ must be at least $1/ (\log n)^{\Theta(w)}$. For constant $w$ this is tight by considering the recursive tribes function, and for other values of $w$ this is nearly tight by considering other read-once AND-OR trees. We generalize this to a \emph{Dice Problem}, where instead of independent tosses of a coin we are given independent tosses of one of two $m$-sided dice. We prove that if the distributions are too close and the mass of each side of the dice is not too small, then the dice cannot be distinguished by small-width read-once branching programs. We suggest one application for this kind of theorems: we prove that Nisan's Generator fools width-$w$ read-once \emph{regular} branching programs, using seed length $O(w^4 \log n \log \log n + \log n \log (1/\eps))$. For $w=\eps=\Theta(1)$, this seed length is $O(\log n \log \log n)$. The coin theorem and its relatives might have other connections to PRGs. This application is related to the independent, but chronologically-earlier, work of Braver man, Rao, Raz and Yehudayoff~\cite{BRRY}.
TL;DR: In this article, the authors studied the number of lattice points in integer dilates of the rational polytope P={(x1,…,xn)∈R⩾0n:∑k=1nxkak⩽1}, where a1,..,an are positive integers.