Day 23: Maximum Product Subarray | Dynamic Programming | Handling Negatives and Zeros
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Day 23: Maximum Product Subarray | Dynamic Programming | Handling Negatives and Zeros
In this challenge, we solve one of the most popular array and dynamic programming interview problems — Maximum Product Subarray. Unlike typical sum problems, the product becomes tricky because negative numbers and zeros can flip results drastically. To overcome this, we maintain both the current maximum and current minimum product at each ...
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Maximum Subarray Sum Algorithm
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SOLVED: Convert the pseudocode into a C functionThe Maximum Subarray ProblemThe maximum subarray problem involves finding a contiguous part of an array of numbers that adds up to the greatest possible sum.Maximum subarray problem input: a non-empty vector V of n integersOutput: indices b, e such that 0 ≤ b sum(v[b:e]): b = i e = j return (b, e)The two nested loops together iterate O(n^2) times, and the sum operation takes O(n) time, so the total time comp
SOLVED: Convert the pseudocode into a C functionThe Maximum Subarray ProblemThe maximum subarray problem involves finding a contiguous part of an array of numbers that adds up to the greatest possible sum.Maximum subarray problem input: a non-empty vector V of n integersOutput: indices b, e such that 0 ≤ b sum(v[b:e]): b = i e = j return (b, e)The two nested loops together iterate O(n^2) times, and the sum operation takes O(n) time, so the total time comp
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