graph_physarum_transport — GRAPH flow op

• 数据种类:matrix × signal → table

• 调用: import fullseye as fs; fs.ledger.graph_physarum_transport(lengths, supply, dt=0.1, max_iters=5000, tol=1e-06, gap_tol=1e-06)(要直接调用实现,import physarum_search; physarum_search.graph_physarum_transport(lengths, supply, dt=0.1, max_iters=5000, tol=1e-06, gap_tol=1e-06);从台账取用则 opsgraph.get("graph_physarum_transport"))

用法

> 该算子的说明尚无译文,以下照原文给出。

L1 optimal transport (the Beckmann problem) on a weighted graph by the slime mould's tube dynamics.

lengths is a symmetric (n, n) matrix of tube lengths (0 = no tube, diagonal 0) and supply an (n,) vector that sums to 0: mass enters at the nodes with supply > 0 and leaves where it is < 0. The pressures solve the weighted Laplacian with this right-hand side (Kirchhoff), each tube carries Q_e = D_e (p_u - p_v) / L_e and adapts dD_e/dt = |Q_e| - D_e (explicit Euler, step dt). Bonifaci (2017) and Facca, Karrenbauer, Kolev and Mehlhorn (2020) prove that the flow converges to a minimiser of sum_e L_e |q_e| under flow conservation, i.e. to the 1-Wasserstein distance between supply+ and supply- in the graph metric (Facca, Cardin and Putti 2018 for the continuum). With one unit source and one sink this is graph_physarum_path.

Returns cost = sum_e L_e |Q_e| (the transport cost of the current flow: an upper bound on the distance), dual_bound = supply . p' with p' the pressure scaled to be 1-Lipschitz along every tube (Kantorovich–Rubinstein: a lower bound on the distance at any iteration, no external solver needed), gap = cost - dual_bound (0 at the optimum; it closes like tol / dt divided by the smallest non-zero flow, because the scaling is set by the tube whose conductance still lags its flow the most), conductance and flow ((n, n) matrices, flow antisymmetric; the net outflow of every node equals its supply), potential ((n,) pressures, 0 at one node per connected component), iters, converged (max |dD| fell below tol), gap_converged (the run stopped because gap <= gap_tol * cost: the cost is certified within gap_tol of the optimum; when several flows are optimal the conductances keep wandering while the pressures settle, so this is the stop that fires; the gap is checked every 10 iterations), history, cost_history and dual_history ((k, 2): iteration and lower bound at each check).

Raises ValueError: matrix not square / symmetric / finite / non-negative, self-loops, no edge; supply not an (n,) finite numeric vector, all zero, not summing to 0 (relative 1e-9) or unbalanced inside a connected component (mass with nowhere to go); dt outside (0, 1]; max_iters < 1; tol < 0; gap_tol < 0.

参考(示例数据、文献)

• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。

• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。

• 算法的正典(作者、年份)与用途见上面的族使用指南。

可运行的示例(实际调用该算子并已验证的样例)

• poc_physarum_transport — py -3.11 examples/poc_physarum_transport.py

类型可衔接的下一个算子(可接受 table 作为输入)

graph_edge_consensus · graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length

同类别(flow)

graph_physarum_path · physarum_route · physarum_transport_image


*Provenance: physarum_search.py — GRAPH 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.