apollo_searchStart {apollo}  R Documentation 
Given a set of starting values and a range for them, searches for points with a better likelihood and steeper gradients.
apollo_searchStart(
apollo_beta,
apollo_fixed,
apollo_probabilities,
apollo_inputs,
searchStart_settings = NA
)
apollo_beta 
Named numeric vector. Names and values for parameters. 
apollo_fixed 
Character vector. Names (as defined in 
apollo_probabilities 
Function. Returns probabilities of the model to be estimated. Must receive three arguments:

apollo_inputs 
List grouping most common inputs. Created by function apollo_validateInputs. 
searchStart_settings 
List. Contains settings for this function. User input is required for all settings except those with a default or marked as optional.

This function implements a simplified version of the algorithm proposed by Bierlaire, M., Themans, M. & Zufferey, N. (2010), A Heuristic for Nonlinear Global Optimization, INFORMS Journal on Computing, 22(1), pp.5970. The main difference lies in it implementing only two out of three tests on the candidates described by the authors. The implemented algorithm has the following steps.
Randomly draw nCandidates
candidates from an interval given by the user.
Label all candidates with a valid loglikelihood (LL) as active.
Apply bfgsIter
iterations of the BFGS algorithm to each active candidate.
Apply the following tests to each active candidate:
Has the BGFS search converged?
Are the candidate parameters after BFGS closer than dTest
from any other candidate with higher LL?
Is the LL of the candidate after BFGS further than distLL
from a candidate with better LL, and its gradient smaller than gTest
?
Mark any candidates for which at least one test results in yes as inactive.
Go back to step 3, unless only one candidate is active, or the maximum number of iterations (maxStages
) has been reached.
This function will write a CSV file to the working/output directory summarising progress. This file is called modelName
_searchStart.csv .
named vector of model parameters. These are the best values found.