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//! Decision heuristics, phase saving, backtracking, and restarts
use super::*;
impl Solver {
/// Pick next variable to branch on
pub(super) fn pick_branch_var(&mut self) -> Option<Var> {
if self.config.use_lrb_branching {
// Use LRB branching
while let Some(var) = self.lrb.select() {
if !self.trail.is_assigned(var) {
self.lrb.on_assign(var);
return Some(var);
}
}
} else if self.config.use_chb_branching {
// Use CHB branching
// Rebuild heap periodically to reflect score changes
if self.stats.decisions.is_multiple_of(100) {
self.chb.rebuild_heap();
}
while let Some(var) = self.chb.pop_max() {
if !self.trail.is_assigned(var) {
return Some(var);
}
}
} else {
// Use VSIDS branching
while let Some(var) = self.vsids.pop_max() {
if !self.trail.is_assigned(var) {
return Some(var);
}
}
}
None
}
/// Backtrack with phase saving
pub(super) fn backtrack_with_phase_saving(&mut self, level: u32) {
// Collect variables that will be unassigned
let mut unassigned_vars = Vec::new();
// Save phases before backtracking
let phase = &mut self.phase;
let lrb = &mut self.lrb;
self.trail.backtrack_to_with_callback(level, |lit| {
let var = lit.var();
if var.index() < phase.len() {
phase[var.index()] = lit.is_pos();
}
// Re-insert variable into LRB heap
lrb.unassign(var);
unassigned_vars.push(var);
});
// Re-insert unassigned variables into VSIDS and CHB heaps
for var in unassigned_vars {
if !self.vsids.contains(var) {
self.vsids.insert(var);
}
if !self.chb.contains(var) {
self.chb.insert(var);
}
}
}
/// Backtrack to a given level without saving phases
pub(super) fn backtrack(&mut self, level: u32) {
self.trail.backtrack_to(level);
}
/// Compute the Luby sequence value for index i (1-indexed: luby(1)=1, luby(2)=1, ...)
/// Sequence: 1, 1, 2, 1, 1, 2, 4, 1, 1, 2, 1, 1, 2, 4, 8, ...
/// For 0-indexed input, we add 1 internally.
pub(super) fn luby(i: u64) -> u64 {
let i = i + 1; // Convert to 1-indexed
// Find k such that 2^k - 1 >= i
let mut k = 1u32;
while (1u64 << k) - 1 < i {
k += 1;
}
let seq_len = (1u64 << k) - 1;
if i == seq_len {
// i is exactly 2^k - 1, return 2^(k-1)
1u64 << (k - 1)
} else {
// Recurse: luby(i) = luby(i - (2^(k-1) - 1))
// The sequence up to 2^k - 1 is: luby(1..2^(k-1)-1), luby(1..2^(k-1)-1), 2^(k-1)
let half_len = (1u64 << (k - 1)) - 1;
if i <= half_len {
Self::luby(i - 1) // Already 0-indexed internally
} else if i <= 2 * half_len {
Self::luby(i - half_len - 1)
} else {
1u64 << (k - 1)
}
}
}
/// Restart
pub(super) fn restart(&mut self) {
self.stats.restarts += 1;
self.backtrack_with_phase_saving(0);
// Calculate next restart threshold based on strategy
match self.config.restart_strategy {
RestartStrategy::Luby => {
self.luby_index += 1;
self.restart_threshold = self.stats.conflicts
+ Self::luby(self.luby_index) * self.config.restart_interval;
}
RestartStrategy::Geometric => {
let current_interval = if self.restart_threshold > self.stats.conflicts {
self.restart_threshold - self.stats.conflicts
} else {
self.config.restart_interval
};
let next_interval =
(current_interval as f64 * self.config.restart_multiplier) as u64;
self.restart_threshold = self.stats.conflicts + next_interval;
}
RestartStrategy::Glucose => {
// Glucose-style dynamic restarts based on LBD
// Restart when recent average LBD is higher than global average
// For now, use geometric with dynamic adjustment
let current_interval = if self.restart_threshold > self.stats.conflicts {
self.restart_threshold - self.stats.conflicts
} else {
self.config.restart_interval
};
// Adjust based on recent LBD trend
let next_interval = if self.recent_lbd_count > 50 {
let recent_avg = self.recent_lbd_sum / self.recent_lbd_count.max(1);
// If recent LBD is low (good), increase interval; if high, decrease
if recent_avg < 5 {
// Good quality clauses - increase interval
((current_interval as f64) * 1.1) as u64
} else {
// Poor quality clauses - decrease interval
((current_interval as f64) * 0.9) as u64
}
} else {
current_interval
};
self.restart_threshold = self.stats.conflicts + next_interval.max(100);
}
RestartStrategy::LocalLbd => {
// Local restarts based on LBD
// Check if we should do a local restart
self.conflicts_since_local_restart += 1;
if self.conflicts_since_local_restart >= 50 && self.should_local_restart() {
// Perform local restart - backtrack to a safe level, not to 0
let local_level = self.compute_local_restart_level();
self.backtrack_with_phase_saving(local_level);
self.conflicts_since_local_restart = 0;
// Reset recent LBD for next window
self.recent_lbd_sum = 0;
self.recent_lbd_count = 0;
} else {
// Standard restart if too many conflicts
let current_interval = if self.restart_threshold > self.stats.conflicts {
self.restart_threshold - self.stats.conflicts
} else {
self.config.restart_interval
};
self.restart_threshold = self.stats.conflicts + current_interval;
}
return; // Don't do full backtrack to 0
}
}
// Re-add all unassigned variables to VSIDS heap
for i in 0..self.num_vars {
let var = Var::new(i as u32);
if !self.trail.is_assigned(var) && !self.vsids.contains(var) {
self.vsids.insert(var);
}
}
}
/// Check if we should perform a local restart
/// Returns true if recent average LBD is significantly higher than global average
pub(super) fn should_local_restart(&self) -> bool {
if self.recent_lbd_count < 50 || self.global_lbd_count < 100 {
return false;
}
let recent_avg = self.recent_lbd_sum / self.recent_lbd_count.max(1);
let global_avg = self.global_lbd_sum / self.global_lbd_count.max(1);
// Local restart if recent average is 1.25x higher than global average
recent_avg * 4 > global_avg * 5
}
/// Compute the level to backtrack to for local restart
/// Use a level that preserves some of the search progress
pub(super) fn compute_local_restart_level(&self) -> u32 {
let current_level = self.trail.decision_level();
// Backtrack to about 20% of current depth to preserve some work
if current_level > 5 {
current_level / 5
} else {
0
}
}
/// Generate a random u64 using xorshift64
pub(super) fn rand_u64(&mut self) -> u64 {
let mut x = self.rng_state;
x ^= x << 13;
x ^= x >> 7;
x ^= x << 17;
self.rng_state = x;
x
}
/// Generate a random f64 in [0, 1)
pub(super) fn rand_f64(&mut self) -> f64 {
const MAX: f64 = u64::MAX as f64;
(self.rand_u64() as f64) / MAX
}
/// Generate a random boolean with given probability of being true
pub(super) fn rand_bool(&mut self, probability: f64) -> bool {
self.rand_f64() < probability
}
}