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Company Cambridge University Press / Intel / / Country Jordan / / / Event Natural Disaster / / Facility Singapore Management University / New Mexico State University / International Research Centre / Carnegie Mellon University / / IndustryTerm urban transportation / projected subgradient descent algorithm / final product / projection operator / average solution / relaxation algorithms / selected solution / multi-agent systems / ubiquitous computing / near-optimal solutions / feasible solution / agent utilities / near-optimal solution / near-optimal feasible solution / approximation guarantees using polynomialtime algorithms / polynomial-time algorithm / final algorithm / nearoptimal solutions / overall solution / primal and dual solution / real-time / parallelizable algorithm / policy search method / car navigation devices / relaxation-based algorithm / polynomialtime algorithm / feasible solutions / heuristic solutions / on providing provably near-optimal solutions / / NaturalFeature mt. Running SLR / / Organization Cambridge University / Learning Department / New Mexico State University / IDM Programme Office / Singapore Management University / Singapore / School of Information Systems / Carnegie Mellon University / Pittsburgh / Singapore National Research Foundation / International Research Centre / Computer Science Department / / Person Shih-Fen Cheng / Paper / Geoffrey J. Gordon / William Yeoh / Ajay S. Aravamudhan / Ai / / Position Vp / max Vp / representative / ij xit Pm / salesman / value Vp / / Technology approximation guarantees using polynomialtime algorithms / polynomial-time algorithm / Lagrangian relaxation algorithms / final algorithm / mobile phones / DEC-POMDP algorithm / projected subgradient descent algorithm / Machine Learning / polynomialtime algorithm / Lagrangian-relaxation-based algorithm / / SocialTag 
Lagrangian Relaxation for Large-Scale Multi-Agent Planning Geoffrey J. Gordon† , Pradeep Varakantham‡ , William Yeoh∗ , Hoong Chuin Lau‡ , Ajay S. Aravamudhan‡ and Shih-Fen Cheng‡ † Machine Learning Departm