12,276 research outputs found
Approximation Bounds For Minimum Degree Matching
We consider the MINGREEDY strategy for Maximum Cardinality Matching.
MINGREEDY repeatedly selects an edge incident with a node of minimum degree.
For graphs of degree at most we show that MINGREEDY achieves
approximation ratio at least in the worst case
and that this performance is optimal among adaptive priority algorithms in the
vertex model, which include many prominent greedy matching heuristics. Even
when considering expected approximation ratios of randomized greedy strategies,
no better worst case bounds are known for graphs of small degrees.Comment: % CHANGELOG % rev 1 2014-12-02 % - Show that the class APV contains
many prominent greedy matching algorithms. % - Adapt inapproximability bound
for APV-algorithms to a priori knowledge on |V|. % rev 2 2015-10-31 % -
improve performance guarantee of MINGREEDY to be tigh
Selfish Knapsack
We consider a selfish variant of the knapsack problem. In our version, the
items are owned by agents, and each agent can misrepresent the set of items she
owns---either by avoiding reporting some of them (understating), or by
reporting additional ones that do not exist (overstating). Each agent's
objective is to maximize, within the items chosen for inclusion in the
knapsack, the total valuation of her own chosen items. The knapsack problem, in
this context, seeks to minimize the worst-case approximation ratio for social
welfare at equilibrium. We show that a randomized greedy mechanism has
attractive strategic properties: in general, it has a correlated price of
anarchy of (subject to a mild assumption). For overstating-only agents, it
becomes strategyproof; we also provide a matching lower bound of on the
(worst-case) approximation ratio attainable by randomized strategyproof
mechanisms, and show that no deterministic strategyproof mechanism can provide
any constant approximation ratio. We also deal with more specialized
environments. For the case of understating-only agents, we provide a
randomized strategyproof -approximate
mechanism, and a lower bound of . When all
agents but one are honest, we provide a deterministic strategyproof
-approximate mechanism with a matching
lower bound. Finally, we consider a model where agents can misreport their
items' properties rather than existence. Specifically, each agent owns a single
item, whose value-to-size ratio is publicly known, but whose actual value and
size are not. We show that an adaptation of the greedy mechanism is
strategyproof and -approximate, and provide a matching lower bound; we also
show that no deterministic strategyproof mechanism can provide a constant
approximation ratio
On Conceptually Simple Algorithms for Variants of Online Bipartite Matching
We present a series of results regarding conceptually simple algorithms for
bipartite matching in various online and related models. We first consider a
deterministic adversarial model. The best approximation ratio possible for a
one-pass deterministic online algorithm is , which is achieved by any
greedy algorithm. D\"urr et al. recently presented a -pass algorithm called
Category-Advice that achieves approximation ratio . We extend their
algorithm to multiple passes. We prove the exact approximation ratio for the
-pass Category-Advice algorithm for all , and show that the
approximation ratio converges to the inverse of the golden ratio
as goes to infinity. The convergence is
extremely fast --- the -pass Category-Advice algorithm is already within
of the inverse of the golden ratio.
We then consider a natural greedy algorithm in the online stochastic IID
model---MinDegree. This algorithm is an online version of a well-known and
extensively studied offline algorithm MinGreedy. We show that MinDegree cannot
achieve an approximation ratio better than , which is guaranteed by any
consistent greedy algorithm in the known IID model.
Finally, following the work in Besser and Poloczek, we depart from an
adversarial or stochastic ordering and investigate a natural randomized
algorithm (MinRanking) in the priority model. Although the priority model
allows the algorithm to choose the input ordering in a general but well defined
way, this natural algorithm cannot obtain the approximation of the Ranking
algorithm in the ROM model
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