Math and Combinatorics Patterns

Mathematical and combinatorial problems appear frequently in competitive programming and algorithm design. They often hide behind constraints that look like brute-force problems but require algebraic shortcuts to meet time limits. This reference covers eight core pattern families with recognition signals, templates, and pitfalls.

Pattern Recognition Table

Constraint-to-Technique Mapping

• N up to 10^7 and asks about primes: Sieve of Eratosthenes.
• N up to 10^6 and asks "how many ways": Precomputed factorials + modular inverse for nCr.
• "modulo 10^9+7" anywhere in the problem: Modular arithmetic throughout; use modular inverse for division.
• Two players, finite game, no randomness: Sprague-Grundy theorem or direct DP on game states.
• "count subsets satisfying property" with small constraint count (k <= 20): Inclusion-exclusion over 2^k subsets.
• Linear recurrence with large N (10^18): Matrix exponentiation to compute nth term in O(k^3 log N).
• "distribute N items into K groups": Stars and bars, possibly combined with inclusion-exclusion for bounded constraints.

Individual Patterns

Modular Arithmetic

Recognition Signals

• Problem says "answer modulo 10^9+7" or any large prime
• Intermediate values overflow 64-bit integers
• Division is required under a modular context
• Counting problem with astronomically large answers

Core Idea

All arithmetic operations (add, subtract, multiply) distribute over modulo. Division under a prime modulus uses modular inverse: a / b mod p = a * b^(p-2) mod p via Fermat's little theorem. Always reduce intermediate results to prevent overflow.

Python Template

MOD = 10**9 + 7

def mod_add(a: int, b: int) -> int:
    return (a + b) % MOD

def mod_mul(a: int, b: int) -> int:
    return (a * b) % MOD

def mod_pow(base: int, exp: int, mod: int = MOD) -> int:
    result = 1
    base %= mod
    while exp > 0:
        if exp & 1:
            result = result * base % mod
        exp >>= 1
        base = base * base % mod
    return result

def mod_inv(a: int, mod: int = MOD) -> int:
    """Modular inverse via Fermat's little theorem. Requires mod is prime."""
    return mod_pow(a, mod - 2, mod)

def mod_div(a: int, b: int) -> int:
    return mod_mul(a, mod_inv(b))

Key Edge Cases

• Subtraction can go negative: use (a - b + MOD) % MOD
• Zero divisor: mod_inv(0) is undefined; guard against it
• Non-prime modulus: Fermat's theorem does not apply; use extended GCD instead
• Chained multiplications: reduce after every multiply, not just at the end

Common Mistakes

• Forgetting to take mod after subtraction, producing negative values
• Using Fermat's inverse with a composite modulus
• Applying mod to intermediate index calculations where exact values are needed

Sieve of Eratosthenes

Recognition Signals

• "Find all primes up to N" with N up to 10^7
• "Smallest prime factor of every number"
• Need to factorize many numbers efficiently
• "Count of prime factors" across a range

Core Idea

The classic sieve marks composites by iterating through multiples of each prime. The smallest-prime-factor (SPF) variant stores the smallest divisor at each index, enabling O(log N) factorization of any number after an O(N log log N) precompute. For ranges beyond memory, use a segmented sieve.

Python Template

def sieve_primes(n: int) -> list[bool]:
    """Standard boolean sieve. is_prime[i] is True if i is prime."""
    is_prime = [True] * (n + 1)
    is_prime[0] = is_prime[1] = False
    for i in range(2, int(n**0.5) + 1):
        if is_prime[i]:
            for j in range(i * i, n + 1, i):
                is_prime[j] = False
    return is_prime

def smallest_prime_factor(n: int) -> list[int]:
    """SPF sieve for fast factorization."""
    spf = list(range(n + 1))
    for i in range(2, int(n**0.5) + 1):
        if spf[i] == i:  # i is prime
            for j in range(i * i, n + 1, i):
                if spf[j] == j:
                    spf[j] = i
    return spf

def factorize(x: int, spf: list[int]) -> list[int]:
    """Factorize x using precomputed SPF table."""
    factors: list[int] = []
    while x > 1:
        factors.append(spf[x])
        x //= spf[x]
    return factors

Key Edge Cases

• N = 0 or 1: no primes exist; handle boundary
• Memory limit: standard sieve needs O(N) space; switch to segmented sieve above ~10^8
• SPF of 1: not defined; skip or handle separately
• Even numbers: can optimize by treating 2 specially and only sieving odds

Common Mistakes

• Starting inner loop at 2*i instead of i*i (correct but slower)
• Off-by-one on sieve size (allocate n+1 slots)
• Forgetting that sieve[0] and sieve[1] are not prime

GCD / LCM

Recognition Signals

• Problem explicitly mentions GCD or LCM
• "Find the largest number that divides all elements"
• "Smallest number divisible by all elements"
• Equations involving linear combinations of two integers (Bezout's identity)

Core Idea

Euclidean algorithm computes GCD in O(log min(a,b)). LCM derives from GCD: lcm(a,b) = a * b // gcd(a,b). Extended Euclidean finds coefficients x, y such that a*x + b*y = gcd(a,b), which is essential for modular inverse when the modulus is not prime.

Python Template

from math import gcd
from functools import reduce

def lcm(a: int, b: int) -> int:
    return a * b // gcd(a, b)

def lcm_array(arr: list[int]) -> int:
    return reduce(lcm, arr)

def gcd_array(arr: list[int]) -> int:
    return reduce(gcd, arr)

def extended_gcd(a: int, b: int) -> tuple[int, int, int]:
    """Returns (g, x, y) such that a*x + b*y = g = gcd(a, b)."""
    if b == 0:
        return a, 1, 0
    g, x1, y1 = extended_gcd(b, a % b)
    return g, y1, x1 - (a // b) * y1

def mod_inv_ext(a: int, m: int) -> int:
    """Modular inverse via extended GCD. Works for any coprime a, m."""
    g, x, _ = extended_gcd(a % m, m)
    if g != 1:
        raise ValueError(f"Inverse does not exist: gcd({a}, {m}) = {g}")
    return x % m

Key Edge Cases

• gcd(0, x) = x and gcd(0, 0) = 0 by convention
• LCM overflow: compute a // gcd(a,b) * b instead of a * b // gcd(a,b)
• Negative inputs: take absolute values before computing
• Extended GCD with b = 0: base case returns (a, 1, 0)

Common Mistakes

• LCM overflow from multiplying before dividing by GCD
• Assuming modular inverse exists when gcd(a, m) != 1
• Recursion depth for very large inputs (Python's default limit); use iterative version if needed

Binomial Coefficients

Recognition Signals

• "How many ways to choose k items from n"
• "nCr mod p" with large n
• Pascal's triangle or binomial theorem mentioned
• Lattice path counting (grid movement problems)

Core Idea

For small n (up to ~10^6), precompute factorials and inverse factorials modulo a prime, then answer nCr queries in O(1). Pascal's triangle DP works for smaller n without modular arithmetic. Lucas' theorem handles nCr mod p when n is very large but p is small.

Python Template

MOD = 10**9 + 7

def precompute_factorials(n: int) -> tuple[list[in