Fractionally Subadditive Maximization Under an Incremental Knapsack Constraint

Disser, Yann; Klimm, Max; Weckbecker, David

doi:10.1007/978-3-030-92702-8_13

Cited by 1 publication

(2 citation statements)

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“…They considered monotone augmentable objective functions (a class of functions containing monotone submodular functions) subject to a growing cardinality constraint and devise a cardinality-scaling algorithm that is 2.618-competitive. Disser et al [16] expanded on this result by giving an improved lower bound of 2.246 and considering a randomized scaling approach for the problem that achieves a randomized competitive ratio of 1.772. The framework was extended to a budget-constrained variant by Disser et al [15].…”

Section: Related Workmentioning

confidence: 99%

“…Disser et al [16] expanded on this result by giving an improved lower bound of 2.246 and considering a randomized scaling approach for the problem that achieves a randomized competitive ratio of 1.772. The framework was extended to a budget-constrained variant by Disser et al [15]. A detailed treatment of incremental maximization was given by Weckbecker [42].…”

Section: Related Workmentioning

confidence: 99%

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Hiring Secretaries over Time: The Benefit of Concurrent Employment

et al. 2020

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We consider a stochastic online problem where n applicants arrive over time, one per time step. Upon arrival of each applicant their cost per time step is revealed, and we have to fix the duration of employment, starting immediately. This decision is irrevocable, i.e., we can neither extend a contract nor dismiss a candidate once hired. In every time step, at least one candidate needs to be under contract, and our goal is to minimize the total hiring cost, which is the sum of the applicants' costs multiplied with their respective employment durations. We provide a competitive online algorithm for the case that the applicants' costs are drawn independently from a known distribution. Specifically, the algorithm achieves a competitive ratio of 2.965 for the case of uniform distributions. For this case, we give an analytical lower bound of 2 and a computational lower bound of 2.148. We then adapt our algorithm to stay competitive even in settings with one or more of the following restrictions: (i) at most two applicants can be hired concurrently; (ii) the distribution of the applicants' costs is unknown; (iii) the total number n of time steps is unknown. On the other hand, we show that concurrent employment is a necessary feature of competitive algorithms by proving that no algorithm has a competitive ratio better than Ω( √ n/ log n) if concurrent employment is forbidden.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%