pub fn approx(
        value: T,
        approx_factor: f64,
        num_resources: usize,
        num_tasks: usize,
        elapsed: Duration,
    ) -> Self {
        Self {
            value,
            approx_factor,
            num_resources,
            num_tasks,
            elapsed,
            expanded: 0,
            pruned_value: 0,
            pruned_h_value: 0,
            pruned_closed: 0,
            proved_optimal: false,
        }
    }
/// Create new stats for an approximate algorithm. All BnB-related statistics are set to 0 and
/// the solution is not optimal by definition.
Create new stats for an approximate algorithm. All BnB-related statistics are set to 0 and
the solution is not optimal by definition.
Create new stats for an approximate algorithm . All BnB - related statistics are set to 0 and the solution is not optimal by definition .
pub fn approx(
        value: T,
        approx_factor: f64,
        num_resources: usize,
        num_tasks: usize,
        elapsed: Duration,
    ) -> Self {
        Self {
            value,
            approx_factor,
            num_resources,
            num_tasks,
            elapsed,
            expanded: 0,
            pruned_value: 0,
            pruned_h_value: 0,
            pruned_closed: 0,
            proved_optimal: false,
        }
    }
pub fn approx ( value : T , approx_factor : f64 , num_resources : usize , num_tasks : usize , elapsed : Duration , ) -> Self { Self { value , approx_factor , num_resources , num_tasks , elapsed , expanded : 0 , pruned_value : 0 , pruned_h_value : 0 , pruned_closed : 0 , proved_optimal : false , } }
Create new stats for an approximate algorithm.
Create new stats for an approximate algorithm .
value
T
approx_factor
f64
num_resources
usize
num_tasks
usize
elapsed
Duration
value
T
approx_factor
f64
num_resources
usize
num_tasks
usize
elapsed
Duration