TY - GEN
T1 - Simplification and Improvement of MMS Approximation
AU - Akrami, Hannaneh
AU - Garg, Jugal
AU - Sharma, Eklavya
AU - Taki, Setareh
N1 - Jugal Garg and Eklavya Sharma were supported by NSF Grant CCF-1942321.
PY - 2023
Y1 - 2023
N2 - We consider the problem of fairly allocating a set of indivisible goods among n agents with additive valuations, using the popular fairness notion of maximin share (MMS). Since MMS allocations do not always exist, a series of works provided existence and algorithms for approximate MMS allocations. The Garg-Taki algorithm gives the current best approximation factor of (3/4 + 1/12n). Most of these results are based on complicated analyses, especially those providing better than 2/3 factor. Moreover, since no tight example is known of the Garg-Taki algorithm, it is unclear if this is the best factor of this approach. In this paper, we significantly simplify the analysis of this algorithm and also improve the existence guarantee to a factor of (3/4+min(1/36, 3/16n−4)). For small n, this provides a noticeable improvement. Furthermore, we present a tight example of this algorithm, showing that this may be the best factor one can hope for with the current techniques.
AB - We consider the problem of fairly allocating a set of indivisible goods among n agents with additive valuations, using the popular fairness notion of maximin share (MMS). Since MMS allocations do not always exist, a series of works provided existence and algorithms for approximate MMS allocations. The Garg-Taki algorithm gives the current best approximation factor of (3/4 + 1/12n). Most of these results are based on complicated analyses, especially those providing better than 2/3 factor. Moreover, since no tight example is known of the Garg-Taki algorithm, it is unclear if this is the best factor of this approach. In this paper, we significantly simplify the analysis of this algorithm and also improve the existence guarantee to a factor of (3/4+min(1/36, 3/16n−4)). For small n, this provides a noticeable improvement. Furthermore, we present a tight example of this algorithm, showing that this may be the best factor one can hope for with the current techniques.
UR - http://www.scopus.com/inward/record.url?scp=85165928355&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165928355&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2023/276
DO - 10.24963/ijcai.2023/276
M3 - Conference contribution
AN - SCOPUS:85165928355
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2485
EP - 2493
BT - Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
A2 - Elkind, Edith
PB - International Joint Conferences on Artificial Intelligence
T2 - 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Y2 - 19 August 2023 through 25 August 2023
ER -