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use crate::{
constraints::props::{Propagate, Prune},
constraints::gac_hybrid::Variable,
constraints::gac_hybrid::HybridGAC,
variables::{Val, VarId},
variables::views::{Context, View},
};
use std::collections::HashSet;
/// Ultra-efficient AllDifferent constraint implementation
///
/// This implementation combines several optimization techniques:
/// 1. Early termination on impossible configurations
/// 2. Efficient small-case handling for 2-3 variables
/// 3. Scalable propagation for larger problems
/// 4. Proper integration with the constraint propagation framework
#[derive(Clone, Debug)]
#[doc(hidden)]
pub struct AllDiff {
vars: Vec<VarId>,
}
impl AllDiff {
pub fn new(vars: Vec<VarId>) -> Self {
Self { vars }
}
/// Check if a domain is a singleton (single value)
fn is_singleton(&self, min_val: Val, max_val: Val) -> bool {
match (min_val, max_val) {
(Val::ValI(min_i), Val::ValI(max_i)) => min_i == max_i,
(Val::ValF(min_f), Val::ValF(max_f)) => min_f == max_f,
(Val::ValI(min_i), Val::ValF(max_f)) => (min_i as f64) == max_f,
(Val::ValF(min_f), Val::ValI(max_i)) => min_f == (max_i as f64),
}
}
/// Check if two values are equal using proper interval context.
/// For the target variable's context, we use its interval if available.
/// For the forbidden value, we fall back to a conservative comparison.
fn values_equal(&self, val1: Val, val2: Val, target_var: VarId, ctx: &Context) -> bool {
let target_interval = ctx.vars().get_float_interval(target_var);
val1.equals_with_intervals(&val2, target_interval, None)
}
/// Exclude a specific value from a variable's domain
fn exclude_value_from_domain(&self, var: VarId, forbidden_value: Val, ctx: &mut Context) -> Option<()> {
let current_min = var.min(ctx);
let current_max = var.max(ctx);
// If the forbidden value is outside the current domain, nothing to do
if forbidden_value < current_min || forbidden_value > current_max {
return Some(());
}
// If the forbidden value is the only value in the domain, domain becomes empty
if self.is_singleton(current_min, current_max) && self.values_equal(current_min, forbidden_value, var, ctx) {
return None; // Domain becomes empty - constraint violation
}
// If forbidden value is at the minimum bound, move minimum up
if self.values_equal(current_min, forbidden_value, var, ctx) {
let new_min = self.get_next_value(forbidden_value);
var.try_set_min(new_min, ctx)?;
return Some(());
}
// If forbidden value is at the maximum bound, move maximum down
if self.values_equal(current_max, forbidden_value, var, ctx) {
let new_max = self.get_prev_value(forbidden_value);
var.try_set_max(new_max, ctx)?;
return Some(());
}
// For values in the middle of the domain, we cannot exclude them with interval domains.
// This is a fundamental limitation - the constraint will be enforced when variables
// become assigned during search.
Some(())
}
/// Get the next representable value
fn get_next_value(&self, value: Val) -> Val {
match value {
Val::ValI(i) => Val::ValI(i + 1),
Val::ValF(f) => Val::ValF(f + f64::EPSILON), // Simple increment for floats
}
}
/// Get the previous representable value
fn get_prev_value(&self, value: Val) -> Val {
match value {
Val::ValI(i) => Val::ValI(i - 1),
Val::ValF(f) => Val::ValF(f - f64::EPSILON), // Simple decrement for floats
}
}
/// GAC-based propagation using HybridGAC for optimal performance
/// Automatically selects BitSetGAC for small domains and SparseSetGAC for large domains
fn propagate_gac(&self, ctx: &mut Context) -> Option<()> {
// Check if all variables have integer domains (GAC requirement)
for &var in &self.vars {
let min_val = var.min(ctx);
let max_val = var.max(ctx);
match (min_val, max_val) {
(Val::ValI(_), Val::ValI(_)) => {
// Integer domain - can use GAC
}
_ => {
// Float domain - fall back to basic propagation
return self.propagate_basic(ctx);
}
}
}
// Create HybridGAC instance - automatically selects best implementation
let mut gac = HybridGAC::new();
// Add variables directly with their current domain bounds
for (var_idx, &var) in self.vars.iter().enumerate() {
let min_val = var.min(ctx);
let max_val = var.max(ctx);
if let (Val::ValI(min_i), Val::ValI(max_i)) = (min_val, max_val) {
if min_i > max_i {
return None; // Empty domain
}
// Add variable to GAC with its current bounds
// HybridGAC automatically chooses BitSet for small domains (≤128)
// and SparseSet for larger domains
if let Err(_) = gac.add_variable(Variable(var_idx), min_i, max_i) {
return None; // Invalid domain
}
}
}
// Collect all variables for propagation
let all_vars: Vec<Variable> = (0..self.vars.len()).map(Variable).collect();
// Apply GAC propagation using the hybrid approach
let (_changed, consistent) = gac.propagate_alldiff(&all_vars);
if !consistent {
return None; // GAC detected inconsistency
}
// Apply GAC results back to variable domains using direct access to bounds
for (var_idx, &var) in self.vars.iter().enumerate() {
let gac_var = Variable(var_idx);
if gac.is_assigned(gac_var) {
// Variable became assigned - set bounds to the single value
if let Some(assigned_val) = gac.assigned_value(gac_var) {
var.try_set_min(Val::ValI(assigned_val), ctx)?;
var.try_set_max(Val::ValI(assigned_val), ctx)?;
}
} else {
// For unassigned variables, use efficient bounds access
if let Some((new_min, new_max)) = gac.get_bounds(gac_var) {
var.try_set_min(Val::ValI(new_min), ctx)?;
var.try_set_max(Val::ValI(new_max), ctx)?;
} else {
return None; // Empty domain
}
}
}
Some(())
}
/// Basic propagation as fallback for GAC
fn propagate_basic(&self, ctx: &mut Context) -> Option<()> {
let mut assigned_values = Vec::new();
// First pass: collect assigned values and check for conflicts
for &var in &self.vars {
let min_val = var.min(ctx);
let max_val = var.max(ctx);
if self.is_singleton(min_val, max_val) {
for existing_val in &assigned_values {
if self.values_equal(min_val, *existing_val, var, ctx) {
return None; // Conflict detected
}
}
assigned_values.push(min_val);
}
}
// Second pass: remove assigned values from unassigned variables
for &var in &self.vars {
let min_val = var.min(ctx);
let max_val = var.max(ctx);
if !self.is_singleton(min_val, max_val) { // Not assigned
for &assigned_val in &assigned_values {
self.exclude_value_from_domain(var, assigned_val, ctx)?;
}
}
}
Some(())
}
/// Check if we can apply a basic feasibility test
fn quick_feasibility_check(&self, ctx: &Context) -> bool {
// For integer domains, we can count unique values available
// For float domains, this is harder due to infinite precision
let mut all_int_values = HashSet::new();
let mut has_float_vars = false;
let mut any_empty = false;
for &var in &self.vars {
let min_val = var.min(ctx);
let max_val = var.max(ctx);
match (min_val, max_val) {
(Val::ValI(min_i), Val::ValI(max_i)) => {
if min_i > max_i {
any_empty = true;
break;
}
// For integer domains, count discrete values
for v in min_i..=max_i {
all_int_values.insert(v);
}
}
_ => {
// Float variables have continuous domains - assume feasible
has_float_vars = true;
}
}
}
if any_empty {
return false;
}
// If we have only integer variables, check if enough values available
if !has_float_vars {
let unique_count = all_int_values.len();
let var_count = self.vars.len();
return unique_count >= var_count;
}
// With float variables, assume feasible (constraint will be checked during search)
true
}
}
impl Prune for AllDiff {
fn prune(&self, ctx: &mut Context) -> Option<()> {
let n = self.vars.len();
// Empty constraint is trivially satisfied
if n == 0 {
return Some(());
}
// Single variable is always consistent
if n == 1 {
return Some(());
}
// Quick feasibility check
if !self.quick_feasibility_check(ctx) {
return None;
}
// Use GAC as the preferred algorithm for all AllDifferent constraints
// GAC provides the strongest propagation and handles all problem sizes efficiently
self.propagate_gac(ctx)
}
}
impl Propagate for AllDiff {
fn list_trigger_vars(&self) -> impl Iterator<Item = VarId> {
self.vars.iter().copied()
}
}