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Explain the Automatic Solver Choice

Source: R/lap_dispatch.R
explain_dispatch.Rd

assignment(method = "auto") picks a solver from a small set of rules evaluated against one pass over the cost matrix. This reports the decision without solving: which rule fired, the property that triggered it, the rules that were tested first and did not fire, and the internal representation the problem will be handed to the solver in.

Usage

explain_dispatch(
  cost,
  method = "auto",
  maximize = FALSE,
  cardinality = c("complete", "maximum", "fixed"),
  n_matches = NULL,
  unmatched_penalty = NULL
)

# S3 method for class 'dispatch_explanation'
print(x, ...)

# S3 method for class 'dispatch_explanation'
summary(object, ...)

Arguments

cost

Numeric matrix, as passed to assignment(). NA or Inf entries are treated as forbidden.

method

Character scalar. "auto" (default) reports the automatic decision. Naming any other method reports that the choice was explicit and which rule "auto" would have picked instead.

maximize, cardinality, n_matches, unmatched_penalty

As in assignment(). A cardinality other than "complete" appends dummy columns before the solver sees the problem, which changes its shape and sparsity, so the rules are evaluated against the same matrix assignment() would hand the solver.

x

A dispatch_explanation object.

...

Ignored.

object

A dispatch_explanation object.

Value

An object of class dispatch_explanation, a list with elements:

  • method - the solver that will run.

  • explicit - TRUE when method was named rather than dispatched.

  • rule, condition, reason - the rule that fired under "auto".

  • considered - data frame of every rule tested, in order, with whether it fired.

  • n, m, transposed - problem shape, and whether the solver sees the transpose. Rectangular problems are transposed internally so the solver always has at least as many columns as rows.

  • probe - the single-pass summary the rules read.

print() gives the selected solver and reason. summary() returns a summary.dispatch_explanation with all decision and probe fields; printing it also lists the rules tested and the observed cost properties.

Invisibly returns x.

Details

The same rule table drives the dispatch itself, so the report cannot drift from the behaviour.

See also

assignment()

Examples

explain_dispatch(matrix(runif(400), 20, 20))
explain_dispatch(matrix(sample(0:1, 400, TRUE), 20, 20))