diff --git a/tig-algorithms/src/knapsack/mod.rs b/tig-algorithms/src/knapsack/mod.rs
index 33ddf59..cf8aa9c 100644
--- a/tig-algorithms/src/knapsack/mod.rs
+++ b/tig-algorithms/src/knapsack/mod.rs
@@ -126,7 +126,8 @@
 
 // c003_a064
 
-// c003_a065
+pub mod relative_quad_fast;
+pub use relative_quad_fast as c003_a065;
 
 // c003_a066
 
diff --git a/tig-algorithms/src/knapsack/relative_quad_fast/README.md b/tig-algorithms/src/knapsack/relative_quad_fast/README.md
new file mode 100644
index 0000000..5fc6f5e
--- /dev/null
+++ b/tig-algorithms/src/knapsack/relative_quad_fast/README.md
@@ -0,0 +1,23 @@
+# TIG Code Submission
+
+    ## Submission Details
+
+    * **Challenge Name:** knapsack
+    * **Submission Name:** relative_quad_fast
+    * **Copyright:** 2025 syebastian
+    * **Identity of Submitter:** syebastian
+    * **Identity of Creator of Algorithmic Method:** null
+    * **Unique Algorithm Identifier (UAI):** null
+
+    ## License
+
+    The files in this folder are under the following licenses:
+    * TIG Benchmarker Outbound License
+    * TIG Commercial License
+    * TIG Inbound Game License
+    * TIG Innovator Outbound Game License
+    * TIG Open Data License
+    * TIG THV Game License
+
+    Copies of the licenses can be obtained at:  
+    https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses
\ No newline at end of file
diff --git a/tig-algorithms/src/knapsack/relative_quad_fast/mod.rs b/tig-algorithms/src/knapsack/relative_quad_fast/mod.rs
new file mode 100644
index 0000000..2139c9c
--- /dev/null
+++ b/tig-algorithms/src/knapsack/relative_quad_fast/mod.rs
@@ -0,0 +1,232 @@
+use anyhow::Result;
+use rand::{rngs::StdRng, Rng, SeedableRng};
+use serde_json::{Map, Value};
+use tig_challenges::knapsack::*;
+ 
+ fn compute_solution(
+    challenge: &Challenge,
+    contribution_list: &mut [i32],
+    unselected_items: &mut Vec<usize>,
+    rng: &mut StdRng,
+) -> Result<Option<(Solution, i32)>> {
+    let mut selected_items = Vec::new();
+    let mut total_weight = 0;
+    let mut total_value = 0;
+
+    const RCL_MAX: usize = 10;
+
+    let probs: Vec<f32> = (0..RCL_MAX)
+        .map(|rank| 1.0 / ((rank + 1) as f32).exp())
+        .collect();
+
+    let mut acc_probs: Vec<f32> = Vec::with_capacity(RCL_MAX);
+    let mut sum = 0.0;
+    for &prob in &probs {
+        sum += prob;
+        acc_probs.push(sum);
+    }
+    let total_prob_max = sum;
+    let max_item_weight = challenge.weights.iter().max().unwrap();
+
+    let mut item_densities: Vec<(usize, f32)> = unselected_items
+        .iter()
+        .map(|&idx| {
+            let ratio = contribution_list[idx] as f32 / challenge.weights[idx] as f32;
+            (idx, ratio)
+        })
+        .collect();
+
+    while !item_densities.is_empty() {
+        let num_candidates = item_densities.len();
+        if num_candidates < 2 {
+            break;
+        }
+    
+        let actual_rcl_size = num_candidates.min(RCL_MAX);
+
+        let total_prob = if actual_rcl_size == RCL_MAX {
+            total_prob_max
+        } else {
+            acc_probs[actual_rcl_size - 1]
+        };
+        
+        let random_threshold = rng.gen_range(0.0..total_prob);
+        let mut selected_rank = match acc_probs[..actual_rcl_size].binary_search_by(|prob| {
+            prob.partial_cmp(&random_threshold).unwrap()
+        }) { Ok(i) | Err(i) => i };
+        if selected_rank >= actual_rcl_size {
+            selected_rank = actual_rcl_size - 1;
+        }
+
+        item_densities.select_nth_unstable_by(selected_rank, |a, b| {
+            b.1.partial_cmp(&a.1).unwrap()
+        });
+        let selected_item = item_densities[selected_rank].0;
+
+        selected_items.push(selected_item);
+        total_weight += challenge.weights[selected_item];
+        total_value += contribution_list[selected_item];
+
+        unsafe {
+            for x in 0..challenge.difficulty.num_items {
+                *contribution_list.get_unchecked_mut(x) += 
+                    *challenge.interaction_values.get_unchecked(x).get_unchecked(selected_item);
+            }
+        }
+
+        if total_weight + max_item_weight > challenge.max_weight {
+            item_densities.retain(|(idx, _)| {
+                total_weight + challenge.weights[*idx] <= challenge.max_weight && *idx != selected_item
+            });
+        } else {
+            item_densities.swap_remove(selected_rank);
+        }
+
+        unsafe {
+            for density in item_densities.iter_mut() {
+                let interaction = *challenge.interaction_values.get_unchecked(selected_item).get_unchecked(density.0);
+                let w = *challenge.weights.get_unchecked(density.0) as f32;
+                density.1 += interaction as f32 / w;
+            }
+        }
+    }
+    unselected_items.clear();
+    unselected_items.extend(0..challenge.difficulty.num_items);
+
+    let mut sorted_selected = selected_items.clone();
+    sorted_selected.sort_unstable_by(|a, b| b.cmp(a));
+
+    for &selected in &sorted_selected {
+        unselected_items.swap_remove(selected);
+    }
+    
+    let local_search_iterations = 150;
+    for _ in 0..local_search_iterations {
+        let mut improved = false;
+ 
+        let mut feasible_adds = Vec::new();
+        for (i, &cand) in unselected_items.iter().enumerate() {
+            let new_w = total_weight + challenge.weights[cand];
+            let new_val = total_value + contribution_list[cand];
+            if new_w <= challenge.max_weight && new_val >= total_value {
+                feasible_adds.push(i);
+            }
+        }
+        if !feasible_adds.is_empty() {
+            let pick = rng.gen_range(0..feasible_adds.len());
+            let add_idx = feasible_adds[pick];
+            let new_item = unselected_items[add_idx];
+ 
+            unselected_items.swap_remove(add_idx);
+            selected_items.push(new_item);
+ 
+            total_weight += challenge.weights[new_item];
+            total_value += contribution_list[new_item];
+            improved = true;
+ 
+            unsafe {
+                for x in 0..challenge.difficulty.num_items {
+                    *contribution_list.get_unchecked_mut(x) += 
+                        *challenge.interaction_values.get_unchecked(x).get_unchecked(new_item);
+                }
+            }
+        }
+
+        let mut feasible_swaps = Vec::new();
+        for (i, &cand_item) in unselected_items.iter().enumerate() {
+            let min_needed =
+                challenge.weights[cand_item] as i32 - (challenge.max_weight as i32 - total_weight as i32);
+            for (j, &rem_item) in selected_items.iter().enumerate() {
+                let rem_w = challenge.weights[rem_item] as i32;
+                if rem_w < min_needed {
+                    continue;
+                }
+                let val_diff = contribution_list[cand_item]
+                    - contribution_list[rem_item]
+                    - challenge.interaction_values[cand_item][rem_item];
+                if val_diff >= 0 {
+                    feasible_swaps.push((i, j));
+                }
+            }
+        }
+ 
+        if !feasible_swaps.is_empty() {
+            let pick = rng.gen_range(0..feasible_swaps.len());
+            let (unsel_idx, sel_idx) = feasible_swaps[pick];
+            let new_item = unselected_items[unsel_idx];
+            let remove_item = selected_items[sel_idx];
+ 
+            selected_items.swap_remove(sel_idx);
+            unselected_items.swap_remove(unsel_idx);
+            selected_items.push(new_item);
+            unselected_items.push(remove_item);
+ 
+            total_value += contribution_list[new_item]
+                - contribution_list[remove_item]
+                - challenge.interaction_values[new_item][remove_item];
+            total_weight = total_weight + challenge.weights[new_item] - challenge.weights[remove_item];
+            improved = true;
+ 
+            unsafe {
+                for x in 0..challenge.difficulty.num_items {
+                    *contribution_list.get_unchecked_mut(x) += 
+                        *challenge.interaction_values.get_unchecked(x).get_unchecked(new_item) -
+                        *challenge.interaction_values.get_unchecked(x).get_unchecked(remove_item);
+                }
+            }
+        }
+ 
+        if !improved {
+            break;
+        }
+    }
+ 
+    if selected_items.is_empty() {
+        Ok(None)
+    } else {
+        Ok(Some((Solution { items: selected_items }, total_value)))
+    }
+ }
+
+ 
+ 
+
+pub fn solve_challenge(
+    challenge: &Challenge,
+    save_solution: &dyn Fn(&Solution) -> anyhow::Result<()>,
+    hyperparameters: &Option<Map<String, Value>>,
+) -> anyhow::Result<()> {
+    let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(
+        challenge.seed[..8].try_into().unwrap(),
+    ));
+ 
+    let mut best_solution: Option<Solution> = None;
+    let mut best_value = 0;
+
+    for _outer_iter in 0..2 {
+        let mut unselected_items: Vec<usize> = (0..challenge.difficulty.num_items).collect();
+        let mut contribution_list = challenge
+            .values
+            .iter()
+            .map(|&v| v as i32)
+            .collect::<Vec<i32>>();
+ 
+        let sol_result =
+            compute_solution(challenge, &mut contribution_list, &mut unselected_items, &mut rng)?;
+ 
+        let (solution, value) = match sol_result {
+            Some(x) => x,
+            None => continue,
+        };
+ 
+        if value > best_value {
+            best_value = value;
+            best_solution = Some(Solution { items: solution.items.clone() });
+        }
+    }
+ 
+    if let Some(s) = best_solution {
+       let _ = save_solution(&s);
+    }
+    return Ok(());
+ }
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