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تسجيل مستخدم جديدAn Attempt to Design a Better Algorithm for the Uncapacitated Facility Location Problem
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Haotian Jiang
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The uncapacitated facility location has always been an important problem due to its connection to operational research and infrastructure planning. Byrka obtained an algorithm that is parametrized by $gamma$ and proved that it is optimal when $gamma>1.6774$. He also proved that the algorithm achieved an approximation ratio of 1.50. A later work by Shi Li achieved an approximation factor of 1.488. In this research, we studied these algorithms and several related works. Although we didnt improve upon the algorithm of Shi Li, our work did provide some insight into the problem. We also reframed the problem as a vector game, which provided a framework to design balanced algorithms for this problem.
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We first show that a better analysis of the algorithm for The Two-Sage Stochastic Facility Location Problem from Srinivasan cite{sri07} and the algorithm for The Robust Fault Tolerant Facility Location Problem from Byrka et al cite{bgs10} can render
improved approximation factors of 2.206 and alpha+4 where alpha is the maximum number an adversary can close, respectively, and which are the best ratios so far. We then present new models for the soft-capacitated facility location problem with uncertainty and design constant factor approximation algorithms to solve them. We devise the stochastic and robust approaches to handle the uncertainty incorporated into the original model. Explicitly, in this paper we propose two new problem, named The 2-Stage Soft-Capacitated Facility Location Problem and The Robust Soft-Capacitated Facility Location Problem respectively, and present constant factor approximation algorithms for them both. Our method uses reductions between facility location problems and linear-cost models, the randomized thresholding technique of Srinivasan cite{sri07} and the filtering and clustering technique of Byrka et al cite{bgs10}.
In this paper we study the facility location problem in the online with recourse and dynamic algorithm models. In the online with recourse model, clients arrive one by one and our algorithm needs to maintain good solutions at all time steps with only
a few changes to the previously made decisions (called recourse). We show that the classic local search technique can lead to a $(1+sqrt{2}+epsilon)$-competitive online algorithm for facility location with only $Oleft(frac{log n}{epsilon}logfrac1epsilonright)$ amortized facility and client recourse. We then turn to the dynamic algorithm model for the problem, where the main goal is to design fast algorithms that maintain good solutions at all time steps. We show that the result for online facility location, combined with the randomized local search technique of Charikar and Guha [10], leads to an $O(1+sqrt{2}+epsilon)$ approximation dynamic algorithm with amortized update time of $tilde O(n)$ in the incremental setting. Notice that the running time is almost optimal, since in general metric space it takes $Omega(n)$ time to specify a new clients position. The approximation factor of our algorithm also matches the best offline analysis of the classic local search algorithm. Finally, we study the fully dynamic model for facility location, where clients can both arrive and depart. Our main result is an $O(1)$-approximation algorithm in this model with $O(|F|)$ preprocessing time and $O(log^3 D)$ amortized update time for the HST metric spaces. Using the seminal results of Bartal [4] and Fakcharoenphol, Rao and Talwar [17], which show that any arbitrary $N$-point metric space can be embedded into a distribution over HSTs such that the expected distortion is at most $O(log N)$, we obtain a $O(log |F|)$ approximation with preprocessing time of $O(|F|^2log |F|)$ and $O(log^3 D)$ amortized update time.
We study a variant of the uncapacitated facility location problem (UFL), where connection costs of clients are defined by (client specific) concave nondecreasing functions of the connection distance in the underlying metric. A special case capturing
the complexity of this variant is the setting called facility location with penalties where clients may either connect to a facility or pay a (client specific) penalty. We show that the best known approximation algorithms for UFL may be adapted to the concave connection cost setting. The key technical contribution is an argument that the JMS algorithm for UFL may be adapted to provide the same approximation guarantee for the more general concave connection cost variant. We also study the star inventory routing with facility location (SIRPFL) problem that was recently introduced by Jiao and Ravi, which asks to jointly optimize the task of clustering of demand points with the later serving of requests within created clusters. We show that the problem may be reduced to the concave connection cost facility location and substantially improve the approximation ratio for all three variants of SIRPFL.
The study of approximate mechanism design for facility location problems has been in the center of research at the intersection of artificial intelligence and economics for the last decades, largely due to its practical importance in various domains,
such as social planning and clustering. At a high level, the goal is to design mechanisms to select a set of locations on which to build a set of facilities, aiming to optimize some social objective and ensure desirable properties based on the preferences of strategic agents, who might have incentives to misreport their private information such as their locations. This paper presents a comprehensive survey of the significant progress that has been made since the introduction of the problem, highlighting the different variants and methodologies, as well as the most interesting directions for future research.
In this paper we study three previously unstudied variants of the online Facility Location problem, considering an intrinsic scenario when the clients and facilities are not only allowed to arrive to the system, but they can also depart at any moment
. We begin with the study of a natural fully-dynamic online uncapacitated model where clients can be both added and removed. When a client arrives, then it has to be assigned either to an existing facility or to a new facility opened at the clients location. However, when a client who has been also one of the open facilities is to be removed, then our model has to allow to reconnect all clients that have been connected to that removed facility. In this model, we present an optimal O(log n_act / log log n_act)-competitive algorithm, where n_act is the number of active clients at the end of the input sequence. Next, we turn our attention to the capacitated Facility Location problem. We first note that if no deletions are allowed, then one can achieve an optimal competitive ratio of O(log n/ log log n), where n is the length of the sequence. However, when deletions are allowed, the capacitated version of the problem is significantly more challenging than the uncapacitated one. We show that still, using a more sophisticated algorithmic approach, one can obtain an online O(log m + log c log n)-competitive algorithm for the capacitated Facility Location problem in the fully dynamic model, where m is number of points in the input metric and c is the capacity of any open facility.
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