From f1d125860f87dc0de2a71922faf2424b601e5148 Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Wed, 28 Jan 2026 19:45:44 +0000
Subject: [PATCH] Optimize fibonacci
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Primary benefit — dramatic runtime improvement: the optimized version reduces total runtime from ~3.26 ms to ~48.2 μs (a ~6658% speedup) by eliminating the huge amount of duplicated work in the naive recursive algorithm.

What changed (specific optimizations)
- Added per-call memoization using a Map. The optimized fibonacci creates a Map cache and an inner fib(k) that:
  - returns k for the base case k <= 1 (same as before),
  - checks cache.get(k) and returns a cached result when present,
  - computes result = fib(k-1) + fib(k-2), stores it with cache.set(k, result), and returns it.
- The recursion structure is preserved (same call shape/stack depth), but repeated subcalls are avoided by caching each distinct k once.

Why this speeds things up
- The original naive recursion recomputes the same Fibonacci numbers exponentially many times: time complexity ~O(phi^n). The memoized version computes each distinct Fibonacci value up to n only once, giving an overall time complexity ~O(n) for distinct inputs (with O(n) extra space for the cache).
- Map.get / Map.set are O(1) on average, so the overhead per computed Fibonacci number is tiny compared to re-running exponential subtrees.
- Practical result in your tests: large inputs (e.g. n=20, n=25) go from milliseconds of work to microseconds. This is where the optimization yields its huge win.

Key behavioral/dep changes and trade-offs
- Behavior: Function semantics are preserved for the tested inputs (including floats, negative numbers, and JS coercions), and the cache is local to each fibonacci(n) call (no global state). Idempotence across calls is preserved.
- Memory: uses O(n) extra memory for the cache per call.
- Overhead on tiny inputs: microbenchmarks for very small n (0, 1, 2, etc.) show small slowdowns (roughly ~40–50% slower in some of your microtests). This is expected and reasonable because each call now pays the cost of creating a Map, an inner function closure and doing Map.get/set checks — overhead that is negligible for medium/large n but visible for trivial calls.
- Trade-off framed positively: the small per-call overhead is an acceptable trade for massive runtime gains on larger inputs and in hot codepaths that call fibonacci with non-trivial n (as evidenced by the 38705% faster result on n=20 in the annotated tests).

Impact on workloads and hot paths
- If fibonacci(n) is called with moderate-to-large n or inside loops, the optimization will substantially increase throughput and reduce latency because repeated subcomputations are removed.
- If the hot path is lots of very small calls and you need minimal per-call overhead, consider:
  - reusing a shared cache across calls (if safe for your application), or
  - switching to an iterative implementation to avoid closure/Map allocation overhead.
- Recursion depth is unchanged — this keeps existing stack characteristics intact (no tail-call elimination).

Tests this optimization is good for
- Large/medium n: huge wins (annotated tests show ms→μs).
- Multiple calls with overlapping inputs in the same call: big wins because cached values within a single fibonacci(n) invocation are reused.
- Cases that rely on purity / no shared state: preserved because cache is per-call.

Summary
- The optimized version turns exponential wasted work into linear work by memoizing subresults with a Map. That yields the large runtime improvement measured in your benchmarks. The only notable trade-off is a small fixed cost per call (Map + closure) that may slow down trivial inputs slightly; this is generally acceptable given the orders-of-magnitude improvement for real workloads and hot paths that exercise non-trivial n.
---
 .../js/code_to_optimize_js_esm/fibonacci.js      | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/code_to_optimize/js/code_to_optimize_js_esm/fibonacci.js b/code_to_optimize/js/code_to_optimize_js_esm/fibonacci.js
index 0ee526315..50e4ee39a 100644
--- a/code_to_optimize/js/code_to_optimize_js_esm/fibonacci.js
+++ b/code_to_optimize/js/code_to_optimize_js_esm/fibonacci.js
@@ -10,10 +10,20 @@
  * @returns {number} The nth Fibonacci number
  */
 export function fibonacci(n) {
-    if (n <= 1) {
-        return n;
+    const cache = new Map();
+    function fib(k) {
+        if (k <= 1) {
+            return k;
+        }
+        const cached = cache.get(k);
+        if (cached !== undefined) {
+            return cached;
+        }
+        const result = fib(k - 1) + fib(k - 2);
+        cache.set(k, result);
+        return result;
     }
-    return fibonacci(n - 1) + fibonacci(n - 2);
+    return fib(n);
 }
 
 /**