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Merge remote-tracking branch 'public/satisfiability/sat_global_opt'
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FiveMovesAhead committed Dec 22, 2024
2 parents e57fd57 + dc80896 commit 2debb3f
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3 changes: 2 additions & 1 deletion tig-algorithms/src/satisfiability/mod.rs
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Expand Up @@ -86,7 +86,8 @@ pub use sat_global as c001_a034;

// c001_a040

// c001_a041
pub mod sat_global_opt;
pub use sat_global_opt as c001_a041;

// c001_a042

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@@ -0,0 +1,292 @@
/*!
Copyright 2024 syebastian
Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you
may not use this file except in compliance with the License. You may obtain a copy
of the License at
https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses
Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the specific
language governing permissions and limitations under the License.
*/

use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng};
use std::collections::HashMap;
use tig_challenges::satisfiability::*;

pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result<Option<Solution>> {
let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64);

let mut p_single = vec![false; challenge.difficulty.num_variables];
let mut n_single = vec![false; challenge.difficulty.num_variables];

let mut clauses_ = challenge.clauses.clone();
let mut clauses: Vec<Vec<i32>> = Vec::with_capacity(clauses_.len());

let mut rounds = 0;

let mut dead = false;

while !(dead) {
let mut done = true;
for c in &clauses_ {
let mut c_: Vec<i32> = Vec::with_capacity(c.len()); // Preallocate with capacity
let mut skip = false;
for (i, l) in c.iter().enumerate() {
if (p_single[(l.abs() - 1) as usize] && *l > 0)
|| (n_single[(l.abs() - 1) as usize] && *l < 0)
|| c[(i + 1)..].contains(&-l)
{
skip = true;
break;
} else if p_single[(l.abs() - 1) as usize]
|| n_single[(l.abs() - 1) as usize]
|| c[(i + 1)..].contains(&l)
{
done = false;
continue;
} else {
c_.push(*l);
}
}
if skip {
done = false;
continue;
};
match c_[..] {
[l] => {
done = false;
if l > 0 {
if n_single[(l.abs() - 1) as usize] {
dead = true;
break;
} else {
p_single[(l.abs() - 1) as usize] = true;
}
} else {
if p_single[(l.abs() - 1) as usize] {
dead = true;
break;
} else {
n_single[(l.abs() - 1) as usize] = true;
}
}
}
[] => {
dead = true;
break;
}
_ => {
clauses.push(c_);
}
}
}
if done {
break;
} else {
clauses_ = clauses;
clauses = Vec::with_capacity(clauses_.len());
}
}

if dead {
return Ok(None);
}

let num_variables = challenge.difficulty.num_variables;
let num_clauses = clauses.len();

let mut p_clauses: Vec<Vec<usize>> = vec![Vec::new(); num_variables];
let mut n_clauses: Vec<Vec<usize>> = vec![Vec::new(); num_variables];

// Preallocate capacity for p_clauses and n_clauses
for c in &clauses {
for &l in c {
let var = (l.abs() - 1) as usize;
if l > 0 {
if p_clauses[var].capacity() == 0 {
p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1);
}
} else {
if n_clauses[var].capacity() == 0 {
n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1);
}
}
}
}

for (i, &ref c) in clauses.iter().enumerate() {
for &l in c {
let var = (l.abs() - 1) as usize;
if l > 0 {
p_clauses[var].push(i);
} else {
n_clauses[var].push(i);
}
}
}

let mut variables = vec![false; num_variables];
for v in 0..num_variables {
let num_p = p_clauses[v].len();
let num_n = n_clauses[v].len();

let nad = 1.28;
let mut vad = nad + 1.0;
if num_n > 0 {
vad = num_p as f32 / num_n as f32;
}

if vad <= nad {
variables[v] = false;
} else {
let prob = num_p as f64 / (num_p + num_n).max(1) as f64;
variables[v] = rng.gen_bool(prob)
}
}

let mut num_good_so_far: Vec<u8> = vec![0; num_clauses];
for (i, &ref c) in clauses.iter().enumerate() {
for &l in c {
let var = (l.abs() - 1) as usize;
if l > 0 && variables[var] {
num_good_so_far[i] += 1
} else if l < 0 && !variables[var] {
num_good_so_far[i] += 1
}
}
}


let mut residual_ = Vec::with_capacity(num_clauses);
let mut residual_indices = vec![None; num_clauses];

for (i, &num_good) in num_good_so_far.iter().enumerate() {
if num_good == 0 {
residual_.push(i);
residual_indices[i] = Some(residual_.len() - 1);
}
}

let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64;
let num_vars = challenge.difficulty.num_variables as f64;
let max_fuel = 2000000000.0;
let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars;
let flip_fuel = 350.0 + 0.9 * clauses_ratio;
let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize;
loop {
if !residual_.is_empty() {

let rand_val = rng.gen::<usize>();

let i = residual_[rand_val % residual_.len()];
let mut min_sad = clauses.len();
let mut v_min_sad = usize::MAX;
let c = &mut clauses[i];

if c.len() > 1 {
let random_index = rand_val % c.len();
c.swap(0, random_index);
}
for &l in c.iter() {
let abs_l = l.abs() as usize - 1;
let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] };

let mut sad = 0;
for &c in clauses_to_check {
if num_good_so_far[c] == 1 {
sad += 1;
}
}

if sad < min_sad {
min_sad = sad;
v_min_sad = abs_l;
}
}

let v = if min_sad == 0 {
v_min_sad
} else if rng.gen_bool(0.5) {
c[0].abs() as usize - 1
} else {
v_min_sad
};

if variables[v] {
for &c in &n_clauses[v] {
num_good_so_far[c] += 1;
if num_good_so_far[c] == 1 {
let i = residual_indices[c].take().unwrap();
let last = residual_.pop().unwrap();
if i < residual_.len() {
residual_[i] = last;
residual_indices[last] = Some(i);
}
}
}
for &c in &p_clauses[v] {
if num_good_so_far[c] == 1 {
residual_.push(c);
residual_indices[c] = Some(residual_.len() - 1);
}
num_good_so_far[c] -= 1;
}
} else {
for &c in &n_clauses[v] {
if num_good_so_far[c] == 1 {
residual_.push(c);
residual_indices[c] = Some(residual_.len() - 1);
}
num_good_so_far[c] -= 1;
}

for &c in &p_clauses[v] {
num_good_so_far[c] += 1;
if num_good_so_far[c] == 1 {
let i = residual_indices[c].take().unwrap();
let last = residual_.pop().unwrap();
if i < residual_.len() {
residual_[i] = last;
residual_indices[last] = Some(i);
}
}
}
}

variables[v] = !variables[v];
} else {
break;
}
rounds += 1;
if rounds >= max_num_rounds {
return Ok(None);
}
}
return Ok(Some(Solution { variables }));
}

#[cfg(feature = "cuda")]
mod gpu_optimisation {
use super::*;
use cudarc::driver::*;
use std::{collections::HashMap, sync::Arc};
use tig_challenges::CudaKernel;

// set KERNEL to None if algorithm only has a CPU implementation
pub const KERNEL: Option<CudaKernel> = None;

// Important! your GPU and CPU version of the algorithm should return the same result
pub fn cuda_solve_challenge(
challenge: &Challenge,
dev: &Arc<CudaDevice>,
mut funcs: HashMap<&'static str, CudaFunction>,
) -> anyhow::Result<Option<Solution>> {
solve_challenge(challenge)
}
}
#[cfg(feature = "cuda")]
pub use gpu_optimisation::{cuda_solve_challenge, KERNEL};
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