"Player 0xd82e010eaaa8e294ee3872d052279692e0ef5d37 submitted 'greedy_…

…quadkp' for challenge knapsack"

tig-algorithms/src/knapsack/greedy_quadkp/benchmarker_outbound.rs

@@ -0,0 +1,167 @@
+/*!
+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 anyhow::Result;
+use tig_challenges::knapsack::{Challenge, Solution};
+
+pub fn solve_challenge(challenge: &Challenge) -> Result<Option<Solution>> {
+    let vertex_count = challenge.weights.len();
+
+    let mut edge_costs: Vec<(usize, f32)> = (0..vertex_count)
+        .map(|flow_index| {
+            let total_flow = challenge.values[flow_index] as i32 + 
+                challenge.interaction_values[flow_index].iter().sum::<i32>();
+            let cost = total_flow as f32 / challenge.weights[flow_index] as f32;
+            (flow_index, cost)
+        })
+        .collect();
+
+    edge_costs.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
+
+    let mut coloring = Vec::with_capacity(vertex_count);
+    let mut uncolored = Vec::with_capacity(vertex_count);
+    let mut current_entropy = 0;
+    let mut current_temperature = 0;
+
+    for &(flow_index, _) in &edge_costs {
+        if current_entropy + challenge.weights[flow_index] <= challenge.max_weight {
+            current_entropy += challenge.weights[flow_index];
+            current_temperature += challenge.values[flow_index] as i32;
+
+            for &colored in &coloring {
+                current_temperature += challenge.interaction_values[flow_index][colored];
+            }
+            coloring.push(flow_index);
+        } else {
+            uncolored.push(flow_index);
+        }
+    }
+
+    let mut mutation_rates = vec![0; vertex_count];
+    for flow_index in 0..vertex_count {
+        mutation_rates[flow_index] = challenge.values[flow_index] as i32;
+        for &colored in &coloring {
+            mutation_rates[flow_index] += challenge.interaction_values[flow_index][colored];
+        }
+    }
+
+    let max_generations = 100;
+    let mut cooling_schedule = vec![0; vertex_count];
+
+    for _ in 0..max_generations {
+        let mut best_mutation = 0;
+        let mut best_crossover = None;
+
+        for uncolored_index in 0..uncolored.len() {
+            let mutant = uncolored[uncolored_index];
+            if cooling_schedule[mutant] > 0 {
+                continue;
+            }
+
+            unsafe {
+                let mutant_fitness = *mutation_rates.get_unchecked(mutant);
+                let min_entropy_reduction = *challenge.weights.get_unchecked(mutant) as i32 - (challenge.max_weight as i32 - current_entropy as i32);
+
+                if mutant_fitness < best_mutation {
+                    continue;
+                }
+
+                for colored_index in 0..coloring.len() {
+                    let gene_to_remove = *coloring.get_unchecked(colored_index);
+                    if *cooling_schedule.get_unchecked(gene_to_remove) > 0 {
+                        continue;
+                    }
+
+                    if min_entropy_reduction > 0 {
+                        let removed_entropy = *challenge.weights.get_unchecked(gene_to_remove) as i32;
+                        if removed_entropy < min_entropy_reduction {
+                            continue;
+                        }
+                    }
+
+                    let fitness_change = mutant_fitness - *mutation_rates.get_unchecked(gene_to_remove)
+                    - *challenge.interaction_values.get_unchecked(mutant).get_unchecked(gene_to_remove);
+
+                    if fitness_change > best_mutation {
+                        best_mutation = fitness_change;
+                        best_crossover = Some((uncolored_index, colored_index));
+                    }
+                }
+            }
+        }
+
+        if let Some((uncolored_index, colored_index)) = best_crossover {
+            let gene_to_add = uncolored[uncolored_index];
+            let gene_to_remove = coloring[colored_index];
+
+            coloring.swap_remove(colored_index);
+            uncolored.swap_remove(uncolored_index);
+            coloring.push(gene_to_add);
+            uncolored.push(gene_to_remove);
+
+            current_temperature += best_mutation;
+            current_entropy = current_entropy + challenge.weights[gene_to_add] - challenge.weights[gene_to_remove];
+
+            unsafe {
+                for flow_index in 0..vertex_count {
+                    *mutation_rates.get_unchecked_mut(flow_index) += 
+                        challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_add) - 
+                        challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_remove);
+                }
+            }
+
+            cooling_schedule[gene_to_add] = 3;
+            cooling_schedule[gene_to_remove] = 3;
+        } else {
+            break;
+        }
+
+        if current_temperature as u32 >= challenge.min_value {
+            return Ok(Some(Solution { items: coloring }));
+        } 
+
+        for cooling_rate in cooling_schedule.iter_mut() {
+            *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 };
+        }
+    }
+
+    if current_temperature as u32 >= challenge.min_value {
+        Ok(Some(Solution { items: coloring }))
+    } else {
+        Ok(None)
+    }
+}
+
+#[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};

tig-algorithms/src/knapsack/greedy_quadkp/commercial.rs

@@ -0,0 +1,167 @@
+/*!
+Copyright 2024 syebastian
+
+Licensed under the TIG Commercial 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 anyhow::Result;
+use tig_challenges::knapsack::{Challenge, Solution};
+
+pub fn solve_challenge(challenge: &Challenge) -> Result<Option<Solution>> {
+    let vertex_count = challenge.weights.len();
+
+    let mut edge_costs: Vec<(usize, f32)> = (0..vertex_count)
+        .map(|flow_index| {
+            let total_flow = challenge.values[flow_index] as i32 + 
+                challenge.interaction_values[flow_index].iter().sum::<i32>();
+            let cost = total_flow as f32 / challenge.weights[flow_index] as f32;
+            (flow_index, cost)
+        })
+        .collect();
+
+    edge_costs.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
+
+    let mut coloring = Vec::with_capacity(vertex_count);
+    let mut uncolored = Vec::with_capacity(vertex_count);
+    let mut current_entropy = 0;
+    let mut current_temperature = 0;
+
+    for &(flow_index, _) in &edge_costs {
+        if current_entropy + challenge.weights[flow_index] <= challenge.max_weight {
+            current_entropy += challenge.weights[flow_index];
+            current_temperature += challenge.values[flow_index] as i32;
+
+            for &colored in &coloring {
+                current_temperature += challenge.interaction_values[flow_index][colored];
+            }
+            coloring.push(flow_index);
+        } else {
+            uncolored.push(flow_index);
+        }
+    }
+
+    let mut mutation_rates = vec![0; vertex_count];
+    for flow_index in 0..vertex_count {
+        mutation_rates[flow_index] = challenge.values[flow_index] as i32;
+        for &colored in &coloring {
+            mutation_rates[flow_index] += challenge.interaction_values[flow_index][colored];
+        }
+    }
+
+    let max_generations = 100;
+    let mut cooling_schedule = vec![0; vertex_count];
+
+    for _ in 0..max_generations {
+        let mut best_mutation = 0;
+        let mut best_crossover = None;
+
+        for uncolored_index in 0..uncolored.len() {
+            let mutant = uncolored[uncolored_index];
+            if cooling_schedule[mutant] > 0 {
+                continue;
+            }
+
+            unsafe {
+                let mutant_fitness = *mutation_rates.get_unchecked(mutant);
+                let min_entropy_reduction = *challenge.weights.get_unchecked(mutant) as i32 - (challenge.max_weight as i32 - current_entropy as i32);
+
+                if mutant_fitness < best_mutation {
+                    continue;
+                }
+
+                for colored_index in 0..coloring.len() {
+                    let gene_to_remove = *coloring.get_unchecked(colored_index);
+                    if *cooling_schedule.get_unchecked(gene_to_remove) > 0 {
+                        continue;
+                    }
+
+                    if min_entropy_reduction > 0 {
+                        let removed_entropy = *challenge.weights.get_unchecked(gene_to_remove) as i32;
+                        if removed_entropy < min_entropy_reduction {
+                            continue;
+                        }
+                    }
+
+                    let fitness_change = mutant_fitness - *mutation_rates.get_unchecked(gene_to_remove)
+                    - *challenge.interaction_values.get_unchecked(mutant).get_unchecked(gene_to_remove);
+
+                    if fitness_change > best_mutation {
+                        best_mutation = fitness_change;
+                        best_crossover = Some((uncolored_index, colored_index));
+                    }
+                }
+            }
+        }
+
+        if let Some((uncolored_index, colored_index)) = best_crossover {
+            let gene_to_add = uncolored[uncolored_index];
+            let gene_to_remove = coloring[colored_index];
+
+            coloring.swap_remove(colored_index);
+            uncolored.swap_remove(uncolored_index);
+            coloring.push(gene_to_add);
+            uncolored.push(gene_to_remove);
+
+            current_temperature += best_mutation;
+            current_entropy = current_entropy + challenge.weights[gene_to_add] - challenge.weights[gene_to_remove];
+
+            unsafe {
+                for flow_index in 0..vertex_count {
+                    *mutation_rates.get_unchecked_mut(flow_index) += 
+                        challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_add) - 
+                        challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_remove);
+                }
+            }
+
+            cooling_schedule[gene_to_add] = 3;
+            cooling_schedule[gene_to_remove] = 3;
+        } else {
+            break;
+        }
+
+        if current_temperature as u32 >= challenge.min_value {
+            return Ok(Some(Solution { items: coloring }));
+        } 
+
+        for cooling_rate in cooling_schedule.iter_mut() {
+            *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 };
+        }
+    }
+
+    if current_temperature as u32 >= challenge.min_value {
+        Ok(Some(Solution { items: coloring }))
+    } else {
+        Ok(None)
+    }
+}
+
+#[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};