The code used the optimal stopping problem using secretary problem algorithm.

secretary_problem.go

package main

import (
    "fmt"
    "math/rand"
)

func secretaryProblem(n, cutoff int) int {
    // Generate a slice of candidates from 1 to n.
    candidates := make([]int, n)
    for i := 0; i < n; i++ {
        candidates[i] = i + 1
    }
    // Shuffle the order of the candidates randomly.
    for i := range candidates {
        j := rand.Intn(i + 1)
        candidates[i], candidates[j] = candidates[j], candidates[i]
    }
    // Initialize the best candidate seen so far as the first candidate.
    bestCandidate := candidates[0]
    // Go through the first cutoff candidates and update the best candidate if a better one is found.
    for i, candidate := range candidates {
        if i >= cutoff {
            // Stop if the cutoff point is reached.
            break
        }
        if candidate > bestCandidate {
            // Update the best candidate if the current candidate is better.
            bestCandidate = candidate
        }
    }
    // Return the best candidate found.
    return bestCandidate
}

func main() {
    n := 10 // Total number of candidates
    cutoff :=

secretary_problem.py

import random

def secretary_problem(
    n: int, 
    e: int
) -> int:

    """
    Defines a function that solves the secretary problem using a cutoff strategy.
    
    Parameters:
        n (int): the total number of candidates
        e (int): the number of candidates that can be observed before
            making a decision.

    Returns:
        int: the best candidate found.
        
    Example:
        >>> n = 10
        >>> cutoff = 2.71828
        >>> best_candidate = secretary_problem(n, cutoff)
        >>> print(f"The best candidate is {best_candidate}.")
        The best candidate is 9.
        
    """

    # Generate a list of candidates. 
    candidates = list(
        range(
            1, 
            n+1
        )
    )

    # Shuffle the order of the candidates randomly.
    random.shuffle(
        candidates
    )

    # Initialize the best candidate seen so far as the first candidate.
    best_candidate = candidates[0]

    # Go through the first cutoff candidates and update the best candidate 
    # if a better one is found.
    for i, candidate in enumerate(
        candidates
    ):
        if i >= cutoff:
            # Stop if the cutoff point is reached.
            break
        if candidate > best_candidate:
            # Update the best candidate if the current candidate is better.
            best_candidate = candidate
    
     # Return the best candidate found. 
    return best_candidate