runSavi: Run Simmulated Annealing on Groups of Vertices
In nsimone101/savi: An implementation of Simulated Annealing on Groups of Vectors

Description Usage Arguments Value Examples

This is the main function of package. It takes in an existing igraph or parameters to make an igraph with random edges. It then runs simulated annealing to try to find the lowest scoring state of the graph. The "state" of the graph is determined by which vertices are in which vertex group.

runSavi(
  initialGraphState,
  numOfVertexGroups,
  coolingScheduleType,
  coolingScheduleParams,
  scoringFunction,
  addOutput = 0,
  useStrict = FALSE
)

`numOfVertexGroups`	The desired number of vertex groups for the graph. It is required that this number be greater than 1 or the function runSavi will throw an error. It is recommended that the number be less than the number of vertices in the graph.
`coolingScheduleType`	The type of cooling schedule used which can be customized by using the parameter "coolingScheduleParams." There are currently 4 options. The argument "Greedy" implements an algorithm which only moves to a lower (or possibly equal) score. The argument "Log" implements a schedule that cools based on a logarithmic function. The argument "Step" implements a schedule that follows a step function. Finally, the arguement "Adaptive" will implement a cooling schedule that automatically determines when to lower the temperature based on the previous scores.
`coolingScheduleParams`	A vector of parameters that should be based on the coolingScheduleType chosen. For "Greedy," pass the number of iterations to use. For "Log," pass the number of iterations to use, the alpha constant, and the beta constant. Note both alpha and beta must be greater than 0. For "Step," pass the total number of iterations to run, the initial temperature, the number of iterations to run before moving down a step, and the percent to step down with. Note, the percent to step down with must be in the interval (0,1). For "Adaptive," pass the total number of iterations to run, the initial temperature, the number of iterations to run before checking to lower the temperature, the percent to step down with, and the threshold of the difference in scores for the temperature to lower. Note that the number of iterations to run before checking to lower the temperature must be an even integer. Further, the percent to step down with must be in the interval (0,1).
`scoringFunction`	A function that returns a value greater than or equal to zero.
`addOutput`	An integer whose default is zero. If given a number, the code will print out the current temperature and current minimum score found on the multiples of that number.
`useStrict`	A boolean whose default is FALSE. If TRUE, the algorithm will only accept a new proposed state of the graph if its score is strictly less than current state's score. Otherwise, it will accept a new proposed state of the graph if its score is less than or equal to the current state's score.
`initalGraphState`	An igraph with groups assigned to all vertices. This configuration is the inital graph state. If no groups are assigned, runSavi will throw an error.

The graph state with the minimum score, the minimum score, an array of the score at a given iteration, and an array of the temperature at a given iteration.

    runSavi(g,3,"Greedy",10000,myScoringFunction)
    runSavi(g,3,"Log",c(10000,1000,1),myScoringFunction)
    runSavi(g,3,"Step",c(10000,20,1000,0.8),myScoringFunction)
    runSavi(g,3,"Adaptive",c(10000,100,100,0.8,0),myScoringFunction)
    runSavi(g,3,"Adaptive",c(10000,100,100,0.8,0),myScoringFunction,addOutput=100)
    runSavi(g,3,"Adaptive",c(10000,100,100,0.8,0),myScoringFunction,addOutput=100,useStrict=TRUE)