Learning Combinatorial Optimzation

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: