Difference between revisions of "Cross cost grid layout"

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{{Layout algorithm|ru.biosoft.graph.CompartmentCrossCostGridLayouter}}
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{{stub|algorithm details and references}}  
  
 
This is a grid-based algorithm that considers (a) edge-edge crossings, (b) node-edge crossings, (c) node-node crossings, (d) distances between nodes in its cost function . This algorithm uses a weight matrix representing the difference between two sequentially obtained  layouts for computing the costs, takes a greedy algorithm for searching locally optimal solutions and simulated annealing for global optimization.
 
This is a grid-based algorithm that considers (a) edge-edge crossings, (b) node-edge crossings, (c) node-node crossings, (d) distances between nodes in its cost function . This algorithm uses a weight matrix representing the difference between two sequentially obtained  layouts for computing the costs, takes a greedy algorithm for searching locally optimal solutions and simulated annealing for global optimization.
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* Perturbation threshold - real value parameter between 0 and 1 represents probability of layout perturbation during simulated annealing
 
* Perturbation threshold - real value parameter between 0 and 1 represents probability of layout perturbation during simulated annealing
 
* Max distance - positive integer parameter represents maximum repulsive distance expressed in number of grid steps (if distance between two nodes exceeds Max distance then there is no repulsion between them).
 
* Max distance - positive integer parameter represents maximum repulsive distance expressed in number of grid steps (if distance between two nodes exceeds Max distance then there is no repulsion between them).
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==See also==
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* [[Diagram document]]

Latest revision as of 13:39, 2 July 2013

Diagram "Aguda1999_CellCycle" layouted using the cross cost grid layout algorithm.
Title
Cross cost grid layout
Class
CompartmentCrossCostGridLayouter
This page or section is a stub. Please add algorithm details and references here!

This is a grid-based algorithm that considers (a) edge-edge crossings, (b) node-edge crossings, (c) node-node crossings, (d) distances between nodes in its cost function . This algorithm uses a weight matrix representing the difference between two sequentially obtained layouts for computing the costs, takes a greedy algorithm for searching locally optimal solutions and simulated annealing for global optimization.

The following parameters of the algorithm must be set:

  • GridX - positive integer parameter represents horizontal grid step in pixels
  • GridY - positive integer parameter represents vertical grid step in pixels
  • Number of iteration - positive integer parameter represents number of iterations during same temperature in simulated annealing
  • Cooling coefficient - real number between 0 and 1 represents cooling coefficient of simulated annealing (small value decrease computation time but leads to quality loss)
  • Perturbation threshold - real value parameter between 0 and 1 represents probability of layout perturbation during simulated annealing
  • Max distance - positive integer parameter represents maximum repulsive distance expressed in number of grid steps (if distance between two nodes exceeds Max distance then there is no repulsion between them).

[edit] See also

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