5 Strategies to Determine If Person 1 Can Win the Candy Game by Maximizing Score in Python

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💡 Problem Formulation: We are tasked with devising a program that can determine if Person 1 can win a hypothetical ‘candy game’, where players alternately choose from a line of candies with associated scores. Each player aims to maximize their score, and we want to specifically evaluate Person 1’s chances of winning given the sequence of candy scores. For instance, given an input array of scores such as [1, 2, 3], and assuming optimal play, we seek a method to conclude if Person 1 can emerge victorious by taking the maximum score.

Method 1: Dynamic Programming

This method involves creating a two-dimensional array that keeps track of the maximum score a player can achieve from any given subarray of the candy scores. By solving smaller subproblems first and using their solutions to construct the answer for a larger problem, this technique is highly effective for optimization tasks such as our candy game.

Here’s an example:

def can_win_max_score(candies):
    n = len(candies)
    dp = [[0]*n for _ in range(n)]
    for length in range(n):
        for i in range(n - length):
            j = i + length
            x = candies[i] - dp[i+1][j] if i+1 <= j else candies[i]
            y = candies[j] - dp[i][j-1] if i  0

candies = [1, 5, 233, 7]

Output: True

This snippet defines a function can_win_max_score that uses dynamic programming to solve for the maximum score Person 1 can obtain. The two-dimensional array dp is used to store the results of subproblems. If the value at dp[0][n-1] is positive, it indicates that Person 1 can win the game. This method is robust but might be less efficient for large numbers of candies due to its O(n^2) time complexity.

Method 2: Greedy Strategy with Lookahead

The greedy strategy approach involves Person 1 picking the candy that maximizes their immediate score, but with an added lookahead feature to make smarter decisions. Although less optimal than dynamic programming in theory, it can sometimes outperform in cases where following the greedy path coincidentally aligns with optimal play or where dynamic programming is infeasible due to memory constraints.

Here’s an example:

def can_person1_win(candies):
    def score(i, j):
        if i > j:
            return 0
        if i == j:
            return candies[i]
        return max(candies[i] + min(score(i+2, j), score(i+1, j-1)),
                   candies[j] + min(score(i, j-2), score(i+1, j-1)))

    return score(0, len(candies) - 1) > sum(candies) / 2

candies = [1, 10, 7, 6]

Output: False

This code snippet defines a recursive function score that evaluates the best move for Person 1 by anticipating the opponent’s responses. The main function can_person1_win checks if the score returned by score is greater than half of the total candy scores. This approach, while potentially faster on short sequences, can suffer from combinatorial explosion and may not always yield an optimal solution.

Method 3: Memoization of Recursive Solution

This approach improves on the basic recursive solution by caching the results of previous computations, reducing the number of calculations needed. It takes advantage of the overlapping subproblems inherent in the candy game problem, and provides a balance between the computational intensity of dynamic programming and the simplicity of a greedy algorithm.

Here’s an example:

def can_win_candy_game(candies):
    cache = {}
    def dp(i, j):
        if (i, j) not in cache:
            cache[(i, j)] = (candies[i] - (dp(i+1, j) if i+1 <= j else 0)) if i == j else \
                             max(candies[i] - (dp(i+1, j) if i+1 <= j else 0), 
                                 candies[j] - (dp(i, j-1) if i  0

candies = [1, 9, 1]

Output: True

This snippet encapsulates a recursive function dp within can_win_candy_game. The function utilizes a cache to store computed values for each pair of indices (i, j), representing the state of the game at each decision point. The memoization strategy reduces the overall time complexity compared to the naïve recursive approach and can quickly solve larger problem sets without overflowing the stack.

Method 4: Iterative Bottom-up Evaluation

This method involves iteratively solving the game starting from the base cases and working upwards. It is a variation of the dynamic programming approach, but from the perspective of filling the DP table iteratively rather than recursively. This often simplifies the logic and can improve performance by avoiding the overhead of recursion.

Here’s an example:

def can_win_iterative(candies):
    n = len(candies)
    dp = [0 for _ in range(n)]
    for i in range(n-1, -1, -1):
        for j in range(i, n):
            if i == j:
                dp[j] = candies[j]
                dp[j] = max(candies[i] - dp[j], candies[j] - dp[j - 1])
    return dp[-1] > 0

candies = [75, 5, 1, 10]

Output: True

In this method, a single-dimensional list dp is used to iteratively calculate the scores, reusing space efficiently to only keep track of the current row of decisions. Person 1 can win if the last element of the dp array is positive. This is an in-place dynamic programming solution that makes the code more space efficient, offering O(n^2) time complexity with a lower space footprint compared to the traditional dynamic programming approach with a two-dimensional array.

Bonus One-Liner Method 5: Mathematical Analysis

Assuming an alternate version of the game where the total score doesn’t matter and we’re checking basic win conditions, a mathematical analysis and direct computation might be the simplest approach. For some games, player 1’s victory may be determined purely by the distribution of scores. However, this approach is highly context-dependent and may not generalize well to all variants of the candy game.

Here’s an example:

def can_win_by_math(candies):
    return len(candies) % 2 == 1 or sum(candies[::2]) > sum(candies[1::2])

candies = [3, 2, 5, 1]

Output: True

The function can_win_by_math is a one-liner that checks if Person 1 wins by either having an odd number of candies (thus guaranteeing the last pick) or by having a greater sum of scores when picking every second candy starting from the first one. Although this approach is the least flexible, its strength lies in its simplicity and speed for specific game rules.


  • Method 1: Dynamic Programming. Ensures an optimal solution through systematic problem-solving. It might not be suitable for games with a large number of candies due to higher memory use.
  • Method 2: Greedy Strategy with Lookahead. Offers a more intuitive approach, possibly saving computational resources. However, it can lead to a suboptimal solution and is not always reliable for winning the game.
  • Method 3: Memoization of Recursive Solution. Improves upon the basic recursive method by caching subproblem solutions. It addresses the performance issues of recursive methods while still ensuring high accuracy.
  • Method 4: Iterative Bottom-up Evaluation. Provides an efficient incremental approach with lower space complexity. It may also reduce the burden of function call overhead associated with recursive solutions.
  • Method 5: Mathematical Analysis. Offers potentially the fastest solution but is heavily dependent on the specific rules and distribution of candy scores. Its applicability is limited compared to other methods.