提出了第一个almost-linear time的算法,可以判断是否存在cost至多为 F 的flow。允许decremental updates(删边,capacity减少,cost增加)。此外,还能维护强连通分量(SCC) 方法是通过dual perspective,实现了cut-based algorithm,调用 m1+o(1) 次min-ratio cut 问题【这是min ratio cycle的对偶问题,这是我们之前提到过...
m1+o(1) iterations of L1 IPM + combinatorial inner problem + mo(1) amortized time dynamic algorithm for inner problem 老方法只用了 m iterations,现在虽然iteration变多了,但是变简单了,原来的flow circulation可能是很多个cycles,现在只用考虑single flow cycle(好奇是怎么做到的) 问题:为什么是用 L1 norm...
min_cost_flow(G, demand='demand', capacity='capacity', weight='weight') 返回满足有向图G中所有需求的最小成本流。 G是一个有向图,具有边缘成本…
Calculate max flow of the network, and get the flow matrix. If there is flow from cid_i to gid_k then we assign the cid_i to the gid_k; If there is no flow to gid_k, then we assign a new id to gid_k. Algorithm complex is O(min(|V|^2 * totflow, |V|^3 * totcost)...
max_flow_min_cost(G, s, t, capacity='capacity', weight='weight') 返回最小成本的最大(s,t)流。 G是一个具有边缘成本和容量的有向图。有一个源…
min_cost_flow_cost(G, demand='demand', capacity='capacity', weight='weight') 求满足有向图 G 中所有需求的最小成本流的成本。 G 是一个有边成本和容量的有向图,其中节点有需求,即它们想要发送或接收一定量的流量。负需求意味着节点想要发送流量,正需求意味着节点想要接收流量。如果流入每个节点的净流量...
目前,解决数据关联问题的最小代价流(min-cost flow)的方法有3个缺点。第一,计算代价高。第二,假定整个视频作为一个batch给出。第三,内存占用大。 相应的解决方法: ①当重复使用计算的时候,采用一种连续最短路径算法的动态版本,解决数据关联问题。 ②当处理一个出现的数据流时,求解数据关联的最优解。
We discuss two algorithms for determining weighted minimal cost flows: the negative circuit algorithm and the shortest augmenting circuit algorithm. The validity of both algorithms follows from a negative circuit theorem for weighted minimal cost flows. A short discussion of the theoretical and ...
An exact and efficient min-cost flow algorithm for data-association in multi-object tracking - yu-lab-vt/muSSP
I was reading again just for fun about max flow and I remembered a thought I had back in my training days. It is very common to use a modified version of Bellman-Ford to solve the min cost max flow problem, called Shortest Path Faster Algorithm (SPFA, you can read about it on books...