Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/115994
Type: Conference paper
Title: Solving constrained combinatorial optimization problems via MAP inference without high-order penalties
Author: Zhang, Z.
Shi, Q.
McAuley, J.
Wei, W.
Zhang, Y.
Yao, R.
Van Den Hengel, A.
Citation: Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017 / pp.3804-3810
Publisher: AAAI
Issue Date: 2017
Conference Name: Thirty-first AAAI Conference on Artificial Intelligence (AAAI-17) (04 Feb 2017 - 09 Feb 2017 : San Francisco)
Statement of
Responsibility: 
Zhen Zhang, Qinfeng Shi, Julian McAuley, WeiWei, Yanning Zhang, Rui Yao, Anton van den Hengel
Abstract: Solving constrained combinatorial optimization problems via MAP inference is often achieved by introducing extra potential functions for each constraint. This can result in very high order potentials, e.g. a 2nd-order objective with pairwise potentials and a quadratic constraint over all N variables would correspond to an unconstrained objective with an order-N potential. This limits the practicality of such an approach, since inference with high order potentials is tractable only for a few special classes of functions. We propose an approach which is able to solve constrained combinatorial problems using belief propagation without increasing the order. For example, in our scheme the 2nd-order problem above remains order 2 instead of order N. Experiments on applications ranging from foreground detection, image reconstruction, quadratic knapsack, and the M-best solutions problem demonstrate the effectiveness and efficiency of our method. Moreover, we show several situations in which our approach outperforms commercial solvers like CPLEX and others designed for specific constrained MAP inference problems.
Rights: Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
RMID: 0030077251
Grant ID: http://purl.org/au-research/grants/arc/DP140102270
http://purl.org/au-research/grants/arc/DP160100703
Published version: https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14450
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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