TY - GEN
T1 - Advice complexity of priority algorithms
AU - Borodin, Allan
AU - Boyar, Joan
AU - Larsen, Kim S.
AU - Pankratov, Denis
PY - 2018
Y1 - 2018
N2 - The priority model of “greedy-like” algorithms was introduced by Borodin, Nielsen, and Rackoff in 2002. We augment this model by allowing priority algorithms to have access to advice, i.e., side information precomputed by an all-powerful oracle. Obtaining lower bounds in the priority model without advice can be challenging and may involve intricate adversary arguments. Since the priority model with advice is even more powerful, obtaining lower bounds presents additional difficulties. We sidestep these difficulties by developing a general framework of reductions which makes lower-bound proofs relatively straightforward and routine. We start by introducing the Pair Matching problem, for which we are able to prove strong lower bounds in the priority model with advice. We develop a template for constructing a reduction from Pair Matching to other problems in the priority model with advice – this part is technically challenging since the reduction needs to define a valid priority function for Pair Matching while respecting the priority function for the other problem. Finally, we apply the template to obtain lower bounds for a number of standard discrete optimization problems.
AB - The priority model of “greedy-like” algorithms was introduced by Borodin, Nielsen, and Rackoff in 2002. We augment this model by allowing priority algorithms to have access to advice, i.e., side information precomputed by an all-powerful oracle. Obtaining lower bounds in the priority model without advice can be challenging and may involve intricate adversary arguments. Since the priority model with advice is even more powerful, obtaining lower bounds presents additional difficulties. We sidestep these difficulties by developing a general framework of reductions which makes lower-bound proofs relatively straightforward and routine. We start by introducing the Pair Matching problem, for which we are able to prove strong lower bounds in the priority model with advice. We develop a template for constructing a reduction from Pair Matching to other problems in the priority model with advice – this part is technically challenging since the reduction needs to define a valid priority function for Pair Matching while respecting the priority function for the other problem. Finally, we apply the template to obtain lower bounds for a number of standard discrete optimization problems.
U2 - 10.1007/978-3-030-04693-4_5
DO - 10.1007/978-3-030-04693-4_5
M3 - Article in proceedings
AN - SCOPUS:85058468993
SN - 9783030046927
T3 - Lecture Notes in Computer Science
SP - 69
EP - 86
BT - Approximation and Online Algorithms - 16th International Workshop, WAOA 2018, Revised Selected Papers
A2 - Epstein, Leah
A2 - Erlebach, Thomas
PB - Springer
T2 - 16th Workshop on Approximation and Online Algorithms, WAOA 2018
Y2 - 23 August 2018 through 24 August 2018
ER -