ON A ROUTING PROBLEM

By T. Lindvall. Probability in the engineering and informational sciences (Print), 2004

1,524 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d934912789f2724e80a5a0a498195ad104fbbb01

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Citations in the corpus

A review of change point detection methods (2018, background): “Historically, it was introduced for a non-related problem [23] and later applied to change point detection [18, 58, 75].”
Route Planning in Transportation Networks (2015, methodology): “An alternative method for computing shortest paths is the Bellman-Ford algorithm [46,127,198].”
On a Paradox of Traffic Planning (2005, background): “See Bellman (1958), von Falkenhausen (1963), and Pollack and Wiebenson (1960).”
Digraphs - theory, algorithms and applications (2002, methodology): “This algorithm originates from the papers [102] by Bellman, [245] by Ford and [572] by Moore.”
Routing, flow, and capacity design in communication and computer networks (2004, methodology): “The literature on dynamic programming is rich; a brief summary can be found in [Den78]; the reader may consult books such as [Bel58], [BD62], and [Den82] for additional detail.”
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards (2010, methodology): “Since there exist polynomial computation time algorithms such as Dijkstra’s algorithm [22] and Bellman-Ford algorithm [23], [24] for the shortest path problem, we could apply these algorithms to solve (30) with edge cost ˆ θ i − ( L +1)ln n m i . q LLC is thus an efﬁcient policy to solve the…”
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013, methodology): “In our case the nodes have negative values, hence the Bellman-Ford algorithm [48], [49] was employed.”
Finding shortest paths on real road networks: the case for A* (2009, methodology): “Notably lacking from past work is a head-to-head comparison of A* and efficient implementations of Dijkstra.”
2D Euclidean distance transform algorithms: A comparative survey (2008, methodology): “The algorithm in Figure 5 is similar to the so-called Dijkstra’s algorithm [Dijkstra 1959; Moore 1959; Bellman 1958].”
Heuristic shortest path algorithms for transportation applications: State of the art (2006, methodology): “The label-correcting (LC) algorithm [14] [15] [16] [17] uses a list structure to manage the scan eligible node set that needs to be examined during the shortest path tree building process.”
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction (2021, methodology): “When ⊕ = min and ⊗ = +, it recovers the original Bellman-Ford algorithm for the shortest path problem [5].”
Route choice modeling: past, present and future research directions (2009, methodology): “Existing algorithms extend the approach adopted to determine a single shortest path (e.g., Bellman, 1958; Dijkstra, 1959).”
Hazardous materials transportation (2011, methodology): “Hall (1986) showed that in STV networks, one cannot simply set the random arc travel times to their expected values and identify the shortest (expected) travel time by applying standard shortest path algorithms based on Bellman’s equation (Bellman, 1958), such as Djikstra’s algorithm.”
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks (2020, methodology): “Shortest path can be solved by the Bellman-Ford (BF) algorithm (Bellman, 1958) with the following update: We can extend the above intuition to other algorithmic tasks.”
Control Techniques for Complex Networks: Workload (2007, methodology): “Similar successive approximation methods were introduced by Bellman and Ford to solve deterministic optimal routing problems [42, 284].”
Ant Colony Optimization (2001, unknown): “It can be shown that this algorithm converges in finite time to the shortest paths with respect to the used metric if no link cost changes after a given time (Bellman, 1958; Ford & Fulkerson, 1962; Bertsekas & Gallager, 1992); this algorithm is also known as distributed Bellman-Ford.”
Principles of model checking (2008, methodology): “– powerful tools: PRISM (4,000 downloads), MRMC, Petri net tools, Probmela – applications: distributed systems, biology, avionics, . . . c© JPK 10 Probability elsewhere • In performance modelling (Erlang, 1907) – models: typically continuous-time Markov chains – emphasis on steady-state and transien”
HOUDINI: Lifelong Learning as Program Synthesis (2018, methodology): “One way to compute these costs is using the Bellman-Ford shortest-path algorithm [7], whose i-th iteration computes an estimated minimum cost di(u) of travel to each node u in the graph.”