Learning Combinatorial Optimzation: Difference between revisions
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Roles | Roles | ||
Abhi (Graph Theory), | Abhi (Graph Theory), | ||
Alvin (Reinforcement Learning/actual paper) | Alvin (Reinforcement Learning/actual paper) | ||
Pranav (actual paper), | Pranav (actual paper), | ||
Daniel (Conclusion: performance, adv, disadv, criticism) | Daniel (Conclusion: performance, adv, disadv, criticism) | ||
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Minimum Vertex Cover: Given a ‘graph’ G, find the minimum number of vertices to tick, so that every single edge is covered. G=(V,E,w). | Minimum Vertex Cover: Given a ‘graph’ G, find the minimum number of vertices to tick, so that every single edge is covered. G=(V,E,w). | ||
Where G is the Graph, V are the vertices, E is the edge, and w is the set of weights for the edges | Where G is the Graph, V are the vertices, E is the edge, and w is the set of weights for the edges | ||
Maximum Cut: Given a ‘graph’ G, | Maximum Cut: Given a ‘graph’ G, | ||
Travelling Salesman Problem | Travelling Salesman Problem | ||
Revision as of 21:00, 19 March 2018
Learning Combinatorial Optimization Algorithms Over Graphs
Roles
Abhi (Graph Theory),
Alvin (Reinforcement Learning/actual paper)
Pranav (actual paper),
Daniel (Conclusion: performance, adv, disadv, criticism)
Intro
1) Graph Theory (MLP, TSP, Maxcut) - Common Problems to Solve are: Minimum Vertex Cover: Given a ‘graph’ G, find the minimum number of vertices to tick, so that every single edge is covered. G=(V,E,w). Where G is the Graph, V are the vertices, E is the edge, and w is the set of weights for the edges
Maximum Cut: Given a ‘graph’ G,
Travelling Salesman Problem
2) Reinforcement Learning -
Actual Paper:
Conclusions (Performance, advantages, disadvantages): A3C? S2V?
Criticism: