Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAlgorithms and Adaptivity Gaps for Stochastic $k$-TSP
307
0
0.0
(
0
)
Added by
Haotian Jiang
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Given a metric $(V,d)$ and a $textsf{root} in V$, the classic $textsf{$k$-TSP}$ problem is to find a tour originating at the $textsf{root}$ of minimum length that visits at least $k$ nodes in $V$. In this work, motivated by applications where the input to an optimization problem is uncertain, we study two stochast
rate research
Read More
Robust optimization is a widely studied area in operations research, where the algorithm takes as input a range of values and outputs a single solution that performs well for the entire range. Specifically, a robust algorithm aims to minimize regret, defined as the maximum difference between the solutions cost and that of an optimal solution in hindsight once the input has been realized. For graph problems in P, such as shortest path and minimum spanning tree, robust polynomial-time algorithms that obtain a constant approximation on regret are known. In this paper, we study robust algorithms for minimizing regret in NP-hard graph optimization problems, and give constant approximations on regret for the classical traveling salesman and Steiner tree problems.
We consider the problem of designing sublinear time algorithms for estimating the cost of a minimum metric traveling salesman (TSP) tour. Specifically, given access to a $n times n$ distance matrix $D$ that specifies pairwise distances between $n$ points, the goal is to estimate the TSP cost by performing only sublinear (in the size of $D$) queries. For the closely related problem of estimating the weight of a metric minimum spanning tree (MST), it is known that for any $varepsilon > 0$, there exists an $tilde{O}(n/varepsilon^{O(1)})$ time algorithm that returns a $(1 + varepsilon)$-approximate estimate of the MST cost. This result immediately implies an $tilde{O}(n/varepsilon^{O(1)})$ time algorithm to estimate the TSP cost to within a $(2 + varepsilon)$ factor for any $varepsilon > 0$. However, no $o(n^2)$ time algorithms are known to approximate metric TSP to a factor that is strictly better than $2$. On the other hand, there were also no known barriers that rule out the existence of $(1 + varepsilon)$-approximate estimation algorithms for metric TSP with $tilde{O}(n)$ time for any fixed $varepsilon > 0$. In this paper, we make progress on both algorithms and lower bounds for estimating metric TSP cost. We also show that the problem of estimating metric TSP cost is closely connected to the problem of estimating the size of a maximum matching in a graph.
In the submodular cover problem, we are given a non-negative monotone submodular function $f$ over a ground set $E$ of items, and the goal is to choose a smallest subset $S subseteq E$ such that $f(S) = Q$ where $Q = f(E)$. In the stochastic version of the problem, we are given $m$ stochastic items which are different random variables that independently realize to some item in $E$, and the goal is to find a smallest set of stochastic items whose realization $R$ satisfies $f(R) = Q$. The problem captures as a special case the stochastic set cover problem and more generally, stochastic covering integer programs. We define an $r$-round adaptive algorithm to be an algorithm that chooses a permutation of all available items in each round $k in [r]$, and a threshold $tau_k$, and realizes items in the order specified by the permutation until the function value is at least $tau_k$. The permutation for each round $k$ is chosen adaptively based on the realization in the previous rounds, but the ordering inside each round remains fixed regardless of the realizations seen inside the round. Our main result is that for any integer $r$, there exists a poly-time $r$-round adaptive algorithm for stochastic submodular cover whose expected cost is $tilde{O}(Q^{{1}/{r}})$ times the expected cost of a fully adaptive algorithm. Prior to our work, such a result was not known even for the case of $r=1$ and when $f$ is the coverage function. On the other hand, we show that for any $r$, there exist instances of the stochastic submodular cover problem where no $r$-round adaptive algorithm can achieve better than $Omega(Q^{{1}/{r}})$ approximation to the expected cost of a fully adaptive algorithm. Our lower bound result holds even for coverage function and for algorithms with unbounded computational power.
The general adwords problem has remained largely unresolved. We define a subcase called {em $k$-TYPICAL}, $k in Zplus$, as follows: the total budget of all the bidders is sufficient to buy $k$ bids for each bidder. This seems a reasonable assumption for a typical instance, at least for moderate values of $k$. We give a randomized online algorithm, achieving a competitive ratio of $left(1 - {1 over e} - {1 over k} right)$, for this problem. We also give randomized online algorithms for other special cases of adwords. Another subcase, when bids are small compared to budgets, has been of considerable practical significance in ad auctions cite{MSVV}. For this case, we give an optimal randomized online algorithm achieving a competitive ratio of $left(1 - {1 over e} right)$. Previous algorithms for this case were based on LP-duality; the impact of our new approach remains to be seen. The key to these results is a simplification of the proof for RANKING, the optimal algorithm for online bipartite matching, given in cite{KVV}. Our algorithms for adwords can be seen as natural extensions of RANKING.
A string $S[1,n]$ is a power (or tandem repeat) of order $k$ and period $n/k$ if it can decomposed into $k$ consecutive equal-length blocks of letters. Powers and periods are fundamental to string processing, and algorithms for their efficient computation have wide application and are heavily studied. Recently, Fici et al. (Proc. ICALP 2016) defined an {em anti-power} of order $k$ to be a string composed of $k$ pairwise-distinct blocks of the same length ($n/k$, called {em anti-period}). Anti-powers are a natural converse to powers, and are objects of combinatorial interest in their own right. In this paper we initiate the algorithmic study of anti-powers. Given a string $S$, we describe an optimal algorithm for locating all substrings of $S$ that are anti-powers of a specified order. The optimality of the algorithm follows form a combinatorial lemma that provides a lower bound on the number of distinct anti-powers of a given order: we prove that a string of length $n$ can contain $Theta(n^2/k)$ distinct anti-powers of order $k$.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
