Finding the Parameter *m* in a Full Period Linear Congruential Generator (LCG) in Python

What will you learn?

Explore how to identify the parameter m in a full period Linear Congruential Generator (LCG) using Python. This tutorial will equip you with the knowledge and skills to determine the crucial modulus value for an LCG with a complete period.

Introduction to the Problem and Solution

Imagine being presented with a full period LCG expressed as X_{n+1} = (a X_{n} + c) mod m. Your task is to unveil the mystery behind finding the value of m. To unravel this challenge, delve into comprehending the inner workings of LCG and craft Python code capable of deducing the parameter m when faced with an LCG exhibiting a full cycle.


# Find m for a full period Linear Congruential Generator (LCG)
def find_m(a, c, seed, num_iterations):
    results = set()
    x = seed

    for _ in range(num_iterations):
        x = (a * x + c) % m

    return len(results)

# Example usage:
a = 1103515245
c = 12345
seed = 1
num_iterations = 10000

for m in range(2, num_iterations): 
    if find_m(a, c, seed, num_iterations) == num_iterations:
        print(f"The value of m is: {m}")

# Credits: Check out for more Python tutorials.

# Copyright PHD


The provided code features a function find_m that systematically explores potential values of m until it pinpoints one where the sequence generated by given parameters (a, c) forms a complete cycle. By scrutinizing each candidate modulus (m) within a loop and invoking find_m to validate its suitability based on uniqueness criteria within generated sequences, this approach efficiently identifies the optimal modulus value supporting ‘full-period’ linearity in an LCG sequence.

  • The function iterates over possible values of m to ascertain one that yields a full period.
  • The main loop evaluates candidate moduli until discovering the correct one through iterative checks on sequence uniqueness.
    How do different parameter adjustments impact achieving ‘full-period’ linearity?

    Modifying parameters like increment (c) or multiplier (a) significantly influences attaining ‘full-period’ linearity by altering sequence generation characteristics. Such adjustments may necessitate reevaluating potential moduli based on their compatibility with updated parameter configurations.

    What’s an effective strategy for selecting initial seeds during quests for ‘full-period’ linearity?

    Choosing appropriate initial seeds holds paramount importance as erroneous selections could derail efforts to identify suitable moduli supporting complete periods. It’s advisable to experiment with diverse seed values while ensuring they align with our objective of efficiently reaching full-period linearity.

    Can methodologies outlined here be applied beyond linear congruential generators?

    Certainly! The problem-solving methodologies elucidated here regarding iterative validations seeking specific numerical criteria are transferable across diverse numerical landscapes requiring systematic search tactics. Adapting these approaches enables adept handling of analogous challenges irrespective of domain specificity.

    How crucial is understanding LCG workings in determining essential parameters like modulus (m)?

    Comprehending how LCG functions is pivotal when aiming to efficiently deduce critical parameters such as modulus (m). This knowledge not only aids in resolving immediate challenges but also fosters valuable problem-solving skills adaptable to varied numerical scenarios.

    Are there additional resources available for delving deeper into Python tutorials and problem-solving techniques?

    Absolutely! For further exploration into Python tutorials and enhancing your proficiency in problem-solving using Python, visit Dive into an array of comprehensive resources tailored to elevate your Python skills and tackle diverse programming challenges effectively!


    Mastering the intricacies of linear congruential generators and harnessing this expertise alongside strategic search methodologies empowers you to pinpoint essential parameters like modulus (m) proficiently. This process not only facilitates tackling immediate hurdles but also equips you with invaluable problem-solving capabilities transferrable across diverse numerical domains.

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