The permutahedron of series-parallel posets
Discrete Applied Mathematics - Combinatorial Optimization
Single-machine scheduling polyhedra with precedence constraints
Mathematics of Operations Research
Facets of the generalized permutahedron of a poset
Discrete Applied Mathematics - Special issue on models and algorithms for planning and scheduling problems
Convex Optimization
Prediction, Learning, and Games
Prediction, Learning, and Games
Learning Permutations with Exponential Weights
The Journal of Machine Learning Research
Online prediction under submodular constraints
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Two dimensional optimal mechanism design for a sequencing problem
IPCO'13 Proceedings of the 16th international conference on Integer Programming and Combinatorial Optimization
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This paper proposes an algorithm for online linear optimization problem over permutations; the objective of the online algorithm is to find a permutation of {1,…,n} at each trial so as to minimize the "regret" for T trials. The regret of our algorithm is $O(n^2 \sqrt{T \ln n})$ in expectation for any input sequence. A naive implementation requires more than exponential time. On the other hand, our algorithm uses only O(n) space and runs in O(n2) time in each trial. To achieve this complexity, we devise two efficient algorithms as subroutines: One is for minimization of an entropy function over the permutahedronPn, and the other is for randomized rounding over Pn.