Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023
We develop a single rounding algorithm for scheduling on unrelated parallel machines; this algorithm works well with the known linear programming-, quadratic programming-, and convex programming-relaxations for scheduling to minimize completion time, makespan, and other well-studied objective functions. This algorithm leads to the following applications for the general setting of unrelated parallel machines: (i) a bicriteria algorithm for a schedule whose weighted completion-time and makespan simultaneously exhibit the current-best individual approximations for these criteria; (ii) better-than-two approximation guarantees for scheduling to minimize the Lnorm of the vector of machine-loads, for all 1 < p < ; and (iii) the first constant-factor multicriteria approximation algorithms that can handle the weighted completion-time and any given collection of integer Lnorms. Our algorithm has a natural interpretation as a melding of linear-algebraic and probabilistic approaches. Via this view, it yields a common generalization of rounding theorems due to Karp et al. [1987] and Shmoys & Tardos [1993], and leads to improved approximation algorithms for the problem of scheduling with resource-dependent processing times introduced by Grigoriev et al. [2007].
Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023
Vicki L Hanson, Edward H Lichtenstein
Cognitive Psychology
Shyam Marjit, Harshit Singh, et al.
WACV 2025
Vittorio Castelli, Lawrence Bergman, et al.
Knowledge-Based Systems