From 65876d2acf1cf2b7e4b3684dc4bfb456d4c26539 Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Tue, 27 Jan 2026 09:08:11 +0000
Subject: [PATCH] Optimize fibonacci
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Runtime improvement (primary benefit): The optimized version reduces the measured runtime from ~600μs to ~261μs (~129% speedup). That lower execution time is the main reason this change was accepted.

What changed (specific optimizations)
- Fast iterative path for integer inputs: If Number.isInteger(n) is true the function computes Fibonacci with a simple loop (a, b = 0, 1; iterate to n). This is O(n) with constant-time arithmetic and no function-call overhead.
- Module-level memoization cache (Map): _fibCache stores computed Fibonacci values (seeded with 0 and 1). The function checks the cache first and returns cached results immediately for repeated calls.
- Memoized recursion only for non-integers: For inputs that are not integer-valued the code falls back to a memoized recursive helper that uses the same Map to avoid repeated work.

Why this speeds things up (performance reasoning)
- Eliminates exponential recursion for integer inputs: The original implementation used naive recursion fibonacci(n-1)+fibonacci(n-2), which does O(phi^n) calls. The iterative loop replaces that with O(n) work and avoids the heavy function-call overhead and duplicated subcomputations entirely.
- Cuts function-call overhead and stack pressure: Recursion causes many JS function calls and stack frames; the iterative loop has none of that overhead. This matters especially for n >= 20 and repeated calls.
- Cache turns repeated calls into O(1) lookups: The Map.get() short-circuits computation for previously-computed n, so repeated requests (common in hot paths or unit tests) are much faster.
- Memoization reduces recomputation for non-integers too: The fallback recursive helper uses the same cache to prevent repeated subcalls, so even non-integer paths avoid repeated work.

Behavioral / dependency notes (impact on workloads)
- Better for hot paths and repeated calls: Workloads that call fibonacci repeatedly or iterate over ranges (tests that loop from n=0..20, repeated calls) see the largest gains — annotated tests show dramatic speedups in loops and repeated calls.
- Much safer for larger n: The iterative path removes risk of deep recursion and stack overflow for larger integer inputs and gives predictable linear running time.
- Memory/time trade-off: The global Map retains computed values across calls. This slightly increases memory usage but is a good trade-off because it yields significantly lower runtime for repeated or larger inputs.
- Minor regressions for tiny inputs: A few very small cases (e.g., certain single small n or coercion cases) may be slightly slower due to Map lookup and type-check overhead; these are small relative to the overall runtime benefit and are an acceptable trade-off for faster general performance (annotated tests show some tiny slowdowns, e.g., fibonacci(4) and numeric-string cases).
- Preserved behavior for edge cases: The code preserves the original behavior for n <= 1 and handles integer-like floats via Number.isInteger(5.0) => true so they take the fast path. Non-integers still follow the recursive logic but benefit from memoization.

Which tests benefit most
- Tests that call the function repeatedly or in loops (strictly increasing checks, repeated calls, performance tests up to n=30) show the largest wins because they avoid exponential recomputation.
- Large-ish inputs (n >= ~20) and benchmark/performance tests will see dramatic improvement thanks to O(n) iterative behavior and caching.

Summary
The optimized code replaces exponential recursive computation with a linear iterative algorithm for integer inputs and adds a module-level memoization cache. These two changes remove duplicated computation and heavy function-call overhead, yielding the observed ~129% runtime improvement. The few small slowdowns on trivial inputs are a reasonable trade-off for much faster and more predictable performance in real workloads and hot paths.
---
 code_to_optimize_js_esm/fibonacci.js | 30 +++++++++++++++++++++++++---
 1 file changed, 27 insertions(+), 3 deletions(-)

diff --git a/code_to_optimize_js_esm/fibonacci.js b/code_to_optimize_js_esm/fibonacci.js
index 0ee526315..f51c5221a 100644
--- a/code_to_optimize_js_esm/fibonacci.js
+++ b/code_to_optimize_js_esm/fibonacci.js
@@ -1,3 +1,5 @@
+const _fibCache = new Map([[0, 0], [1, 1]]);
+
 /**
  * Fibonacci implementations - ES Module
  * Intentionally inefficient for optimization testing.
@@ -10,10 +12,32 @@
  * @returns {number} The nth Fibonacci number
  */
 export function fibonacci(n) {
-    if (n <= 1) {
-        return n;
+    if (n <= 1) return n;
+
+    if (Number.isInteger(n)) {
+        const cached = _fibCache.get(n);
+        if (cached !== undefined) return cached;
+
+        let a = 0, b = 1;
+        for (let i = 2; i <= n; i++) {
+            const c = a + b;
+            a = b;
+            b = c;
+        }
+
+        _fibCache.set(n, b);
+        return b;
     }
-    return fibonacci(n - 1) + fibonacci(n - 2);
+
+    function memoFib(x) {
+        if (x <= 1) return x;
+        if (_fibCache.has(x)) return _fibCache.get(x);
+        const val = memoFib(x - 1) + memoFib(x - 2);
+        _fibCache.set(x, val);
+        return val;
+    }
+
+    return memoFib(n);
 }
 
 /**