Examples of using Input graph in English and their translations into Russian
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Colloquial
Ting of HIS stream; transmit signal to input graph device;
For each node[math]v[/math]of the input graph, the indicator of reachability from the source node[math]u/math.
The analysis can be improved to within a polynomial factor of the number t( G){\displaystyle t(G)}of spanning trees of the input graph.
The running time is linear in the size of the input graph, and polynomial in its number of sources and sinks.
If the input graphs are restricted to dense instances, with degree Ω( n){\displaystyle\Omega(n)}, there is an FPRAS if x≥ 1, y≥ 1.
The sphere separators constructed in this way partition the input graph into subgraphs of at most n(d+ 1)/(d+ 2) vertices.
It is impossible to exactly estimate the number of nodes in each layer because this number depends on the connectedness structure of the input graph.
However, if edges arrive in a random order, and the input graph has a degree that is at least logarithmic, then smaller competitive ratios can be achieved.
The idea behind this celebrated result of Jerrum andSinclair is to set up a Markov chain whose states are the matchings of the input graph.
If the input graph is not a Helly circular-arc graph, then the algorithm returns a certificate of this fact in the form of a forbidden induced subgraph.
Note right away that, for implementations of such algorithms,the locality depends in many ways on the structure of the input graph and may change significantly.
For each node[math]v[/math] of the input graph, the distance[ math] d( v)[/ math] defined as the number of arcs in the shortest path from[math]u[/math] to[math]v/math.
In order to demonstrate efficiency, we present the graphs of the execution times for different modes of computation as functions of the size of the input graph.
Compute a planar embedding of G. Compute a spanning tree T1 of the input graph G. Give an arbitrary orientation to each edge in G that is also in T1.
This indicates that the problem fits very badly into cache memory, and the program is compelled to work all the time with RAM,which is explained by the very large size of the input graph.
Furthermore, the Boyer-Myrvold test was extended to extract multiple Kuratowski subdivisions of a non-planar input graph in a running time linearly dependent on the output size.
The leaves of the Cartesian tree represent the vertices of the input graph, and the minimax distance between two vertices equals the weight of the Cartesian tree node that is their lowest common ancestor.
Nevertheless, several algorithms are known to compute path-decompositions more efficiently whenthe pathwidth is small, when the class of input graphs is limited, or approximately.
For instance, it can be solved in time linear in the size of the input graph(but exponential in the length of the path), by an algorithm that performs the following steps: Perform a depth-first search of the graph. .
It is possible to find maximum-capacity paths and minimax paths with a single source andsingle destination very efficiently even in models of computation that allow only comparisons of the input graph's edge weights and not arithmetic on them.
Besides, it may depend on the type of the input graph because the structure and the number of strongly connected components affect strongly both the time for finding these components and the time for performing breadth-first searches.
As with treewidth, branchwidth can be used as the basis of dynamic programming algorithms for many NP-hard optimization problems,using an amount of time that is exponential in the width of the input graph or matroid.
Roughly speaking, in order to solve the undirected s-t connectivity problem in logarithmic space, the input graph is transformed, using a combination of powering and the zigzag product, into a constant-degree regular graph with a logarithmic diameter.
The Fraysseix-Rosenstiehl planarity criterion can be used directly as part of algorithms for planarity testing, while Kuratowski's and Wagner's theorems have indirect applications: if an algorithm can find a copy of K5 or K3,3 within a given graph, it can be sure that the input graph is not planar and return without additional computation.
The algorithm of Alon(2003) begins by making the input graph regular, without increasing its degree or significantly increasing its size, by merging pairs of vertices that belong to the same side of the bipartition and then adding a small number of additional vertices and edges.
By the above theorem, this is equivalent to the Feder-Vardi conjecture on CSP dichotomy, which states that for every constraint language Γ, CSP(Γ) is NP-complete or in P. The homomorphism problem with a single fixed graph G on left side of input instances can be solved by brute-force in time| V( H)| O(| V( G)|),so polynomial in the size of the input graph H. In other words, the problem is trivially in P for graphs G of bounded size.
Since counting the number of perfect matchings in a general graph is P-complete,some restriction on the input graph is required unless FP, the function version of P, is equal to P. Counting matchings, which is known as the Hosoya index, is also P-complete even for planar graphs. .
By applying exact algorithms for vertex coloring to the line graph of the input graph, it is possible to optimally edge-color any graph with m edges, regardless of the number of colors needed, in time 2mmO(1) and exponential space, or in time O(2.2461m) and only polynomial space Björklund.
For example, when the output devices of graph A are connected to the input devices of graph B, then B is dependent on A.
ECG graph shows the input signal.