| name | algo-basic-algorithms |
| description | Compute GCD/LCM via Euclid's algorithm, find roots with Newton's method, and count k-mers using dict/Counter vs O(n^2) list-scan. Use for GCD/LCM math, root-finding, k-mer counting, or Big-O complexity questions. |
| tool_type | python |
| primary_tool | Python |
Basic Algorithms
When to Use
- Computing GCD/LCM for fraction simplification, modular arithmetic, or RSA key generation.
- Finding cube roots (or other roots) numerically via Newton-Raphson iteration.
- Counting k-mers in a DNA/RNA sequence and need to justify why a hash map beats a list scan.
- Explaining/verifying Big-O complexity of a nested-loop bioinformatics routine (e.g., all-vs-all pairwise distance) before it is run at genome scale.
- Teaching or reviewing foundational CS algorithms (sorting, ciphers) as building blocks for larger pipelines.
Version Compatibility
Pure Python stdlib — no third-party dependencies. Works on Python ≥3.9 (uses list[str] / dict[str, int] builtin generics).
Prerequisites
- Python ≥3.9. No packages to install.
- Familiarity with Big-O notation (see
## Complexity Table below for concrete numbers).
Complexity Table
| Algorithm | Best | Average | Worst | Space |
|---|
| Euclidean GCD | O(1) | O(log min(a,b)) | O(log min(a,b)) | O(1) |
| Newton's cube root | O(1) | O(log 1/ε) | O(max_iter) | O(1) |
| k-mer counting (dict) | O(n·k) | O(n·k) | O(n·k) | O(n) |
| k-mer counting (list scan) | O(n·k) | O(n²·k) | O(n²·k) | O(n) |
Euclidean GCD / LCM
Goal: compute the greatest common divisor and least common multiple of two integers.
Approach: repeatedly replace (a, b) with (b, a % b) until b == 0; the key identity is gcd(a, b) == gcd(b, a % b).
def gcd(a: int, b: int) -> int:
"""Greatest common divisor via the Euclidean algorithm.
Complexity: O(log min(a, b)) time, O(1) space.
>>> gcd(48, 18)
6
>>> gcd(-48, 18)
6
"""
a, b = abs(a), abs(b)
while b != 0:
a, b = b, a % b
return a
def lcm(a: int, b: int) -> int:
"""Least common multiple, computed from gcd to avoid overflow in
other languages: (a // gcd(a, b)) * b, not (a * b) // gcd(a, b).
"""
if a == 0 or b == 0:
return 0
g = gcd(a, b)
return abs(a // g * b)
Worst case is consecutive Fibonacci numbers (e.g., gcd(89, 55)) — each step still reduces the problem by at least half, so it stays O(log min(a, b)).
Newton's Method — Root Finding
Goal: find x such that x**3 == a (cube root), without **(1/3) (which mishandles negative a).
Approach: iterate x_new = (2*x + a/x**2) / 3, derived from x - f(x)/f'(x) for f(x) = x**3 - a. Convergence is quadratic — correct digits roughly double each iteration.
def cube_root(a: float, epsilon: float = 1e-10, max_iterations: int = 1000) -> float:
"""Cube root of a via Newton-Raphson iteration.
Complexity: O(log(1/epsilon)) iterations due to quadratic convergence.
>>> round(cube_root(27), 6)
3.0
>>> round(cube_root(-8), 6)
-2.0
"""
if a == 0:
return 0.0
sign = 1 if a > 0 else -1
a_abs = abs(a)
x = a_abs / 2 if a_abs > 1 else a_abs
for _ in range(max_iterations):
x_new = (2 * x + a_abs / (x * x)) / 3
if abs(x_new - x) < epsilon:
return sign * x_new
x = x_new
return sign * x
k-mer Counting: Hash Map vs List Scan
Goal: count occurrences of every k-mer in a sequence — the core operation behind genome-size estimation (Jellyfish), error correction, and de Bruijn graph assembly.
Approach: a list-scan implementation looks for an existing (kmer, count) entry by scanning the whole list on every insert — O(n) per lookup, O(n²·k) overall. A dict gives O(1) average lookup, so the same task is O(n·k). This gap is why every real k-mer counter (Jellyfish, KMC, Counter) uses a hash map, not a list.
from collections import Counter
def count_kmers_list(sequence: str, k: int) -> list[tuple[str, int]]:
"""Count k-mers by linear-scanning a list on every insert.
Complexity: O(n^2 * k) time — n-k+1 k-mers, each insert scans up to
n-k+1 existing entries and compares k characters.
"""
counts: list[list] = []
for i in range(len(sequence) - k + 1):
kmer = sequence[i:i + k]
for entry in counts:
if entry[0] == kmer:
entry[1] += 1
break
else:
counts.append([kmer, 1])
return [(kmer, n) for kmer, n in counts]
def count_kmers_dict(sequence: str, k: int) -> dict[str, int]:
"""Count k-mers with a dict (equivalent to collections.Counter).
Complexity: O(n * k) time — n-k+1 k-mers, O(k) average hash/compare
per lookup, O(1) amortized dict insert/update.
"""
counts: dict[str, int] = {}
for i in ((sequence) - k + ):
kmer = sequence[i:i + k]
counts[kmer] = counts.get(kmer, ) +
counts
() -> Counter:
Counter(sequence[i:i + k] i ((sequence) - k + ))
Pitfalls
- Euclidean GCD with negatives: always take
abs() first; gcd(-48, 18) must return 6, not -6.
- LCM overflow: Python ints are arbitrary precision, but in other languages
a * b overflows before dividing — compute (a // gcd(a, b)) * b.
- Newton's method diverges from a bad initial guess:
a/2 works well for a > 1; use a itself for 0 < a <= 1; special-case a == 0 since the update divides by x**2.
- Using float epsilon for exact integer algorithms: GCD needs no tolerance — epsilon-based convergence only applies to numerical methods like Newton's.
- List-scan k-mer counting silently works on toy data, then falls over at genome scale: O(n²·k) is fine for a 100 bp read, catastrophic for a chromosome. Default to
dict/Counter; only justify a list if k-mer cardinality is tiny and fixed.
- k-mer window off-by-one: valid start indices are
range(len(sequence) - k + 1); using range(len(sequence)) reads past the end and raises/truncates silently via slicing.
See Also
bio-database-access-blast-searches — when O(n²) all-vs-all sequence comparison must be replaced with heuristic search at scale.bio-sequence-io-sequence-statistics — sequence-level statistics that often reuse k-mer counting.bio-population-genetics-scikit-allel-analysis — downstream use of counting/complexity tradeoffs on large variant matrices.
