Learning Combinatorial Optimzation: Difference between revisions
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Learning Combinatorial Optimization Algorithms Over Graphs | Learning Combinatorial Optimization Algorithms Over Graphs | ||
Learning Combinatorial Optimization Over Graphs | |||
Roles | |||
Abhi (intro), | |||
Pranav/Avlin (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 optimal solution | |||
Maximum Cut: Given a ‘graph’ G, | |||
Travelling Salesman Problem | |||
2) Reinforcement Learning - | |||
Actual Paper: | |||
Conclusions (Performance, advantages, disadvantages): A3C? S2V? | |||
Criticism: | |||
Revision as of 18:09, 19 March 2018
Learning Combinatorial Optimization Algorithms Over Graphs
Learning Combinatorial Optimization Over Graphs
Roles Abhi (intro), Pranav/Avlin (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 optimal solution Maximum Cut: Given a ‘graph’ G, Travelling Salesman Problem
2) Reinforcement Learning -
Actual Paper:
Conclusions (Performance, advantages, disadvantages): A3C? S2V?
Criticism: