태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The Memetracker corpus contains articles from mainstream media and blogs from August 1 to October 31, 2008 with about 1 million documents per day. It has 10,967 hyperlink cascades among 600 media sites. Source: Marked Temporal Dynamics Modeling based on Recurrent Neural Network Image Source: http://blog.fabric.ch/index.php?/archives/292-Memetracker-Tracking-News-Phrases-over-the-Web.html
Random sampled instances of the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) for 20, 50 and 100 customer nodes. * Coordinates sampled from unit square * demands sampled as integers from range [1, 9] * time windows sampled with: - ready times (TW start) as random integers in time horizon T - due times (TW end) sampled from Normal distribution The dataset is used as a validation and test set to evaluate machine learning based solvers for the CVRPTW. The corresponding open source sample code can be used to sample corresponding training data or additional validation and test data, also for other problem sizes.
Distributional MIPLIB is a dataset of Mixed Integer Linear Programming (MILP) instances designed to advance research on learning to optimize \url{https://www.arxiv.org/abs/2406.06954}. It is a curated dataset of MILP distributions from 13 domains, classified into different hardness levels. The links for downloading the distributions are provided in the webpage of each domain.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.