7,015 research outputs found

    On character sums over flat numbers

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    Let q2q\geqslant2 be an integer, χ\chi be any non-principal character mod qq, and H=H(q)q.H=H(q)\leqslant q. In this paper the authors prove some estimates for character sums of the form W(χ,H;q)=nF(H)χ(n),\mathcal{W}(\chi,H;q)=\sum_{n\in\mathscr{F}(H)}\chi(n), where \mathscr{F}(H)=\left\{n\in\mathbb{Z}|(n,q)=1,1\leqslant n,\bar{n}\leqslant q, |n-\bar{n}|\leqslant H\}, nˉ\bar{n} is defined by nnˉ1(modq).n\bar{n}\equiv1\pmod q.Comment: 9 pages, with a complete proof of Theorem 3, Section 5. Accepted by J. Number Theor

    Dynamic Assortment Optimization with Changing Contextual Information

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    In this paper, we study the dynamic assortment optimization problem under a finite selling season of length TT. At each time period, the seller offers an arriving customer an assortment of substitutable products under a cardinality constraint, and the customer makes the purchase among offered products according to a discrete choice model. Most existing work associates each product with a real-valued fixed mean utility and assumes a multinomial logit choice (MNL) model. In many practical applications, feature/contexutal information of products is readily available. In this paper, we incorporate the feature information by assuming a linear relationship between the mean utility and the feature. In addition, we allow the feature information of products to change over time so that the underlying choice model can also be non-stationary. To solve the dynamic assortment optimization under this changing contextual MNL model, we need to simultaneously learn the underlying unknown coefficient and makes the decision on the assortment. To this end, we develop an upper confidence bound (UCB) based policy and establish the regret bound on the order of O~(dT)\widetilde O(d\sqrt{T}), where dd is the dimension of the feature and O~\widetilde O suppresses logarithmic dependence. We further established the lower bound Ω(dT/K)\Omega(d\sqrt{T}/K) where KK is the cardinality constraint of an offered assortment, which is usually small. When KK is a constant, our policy is optimal up to logarithmic factors. In the exploitation phase of the UCB algorithm, we need to solve a combinatorial optimization for assortment optimization based on the learned information. We further develop an approximation algorithm and an efficient greedy heuristic. The effectiveness of the proposed policy is further demonstrated by our numerical studies.Comment: 4 pages, 4 figures. Minor revision and polishing of presentatio