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Combinatorial Optimization
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Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.
기법
관련 논문에서 소개한 기법입니다. 이 태스크에서의 평가 여부는 평가 결과에서 확인하세요.
Pointer Network
Pointer Networks tackle problems where input and output data are sequential data, but can't be solved by seq2seq type models because discrete categories of output elements depend on the variable input size (and are not decided in advance). A Pointer Network learns the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. They solve the problem of variable size output dictionaries using additive attention. But instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, Pointer Networks use attention as a pointer to select a member of the input sequence as the output. Pointer-Nets can be used to learn approximate solutions to challenging geometric problems such as finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem.
POMO
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Sym-NCO
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
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