How do you solve Find Maximum Value in a Constrained Sequence on LeetCode?

Find Maximum Value in a Constrained Sequence (LeetCode #3796) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Restrictions limit values directly and indirectly.

What is the time complexity of Find Maximum Value in a Constrained Sequence?

The optimal solution for Find Maximum Value in a Constrained Sequence runs in O(n) time and uses O(n) space. The linear complexity comes from two passes through the array to enforce constraints, making it efficient.

What is the brute force solution for Find Maximum Value in a Constrained Sequence?

The brute force approach for Find Maximum Value in a Constrained Sequence is Brute Force, with O(n²) time complexity and O(1) space complexity. This approach tries all possible sequences to find the maximum value. It generates all combinations of values while checking the constraints, which is inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Find Maximum Value in a Constrained Sequence?

Find Maximum Value in a Constrained Sequence uses the following concepts: Array, Greedy. The recommended patterns to study are: Greedy, Dynamic Programming.

What are the interview tips for Find Maximum Value in a Constrained Sequence?

Explain your thought process clearly. Consider edge cases and constraints. Practice similar problems to build intuition.

What are common mistakes when solving Find Maximum Value in a Constrained Sequence?

Ignoring indirect effects of restrictions. Not properly handling boundary conditions.

#3796

Find Maximum Value in a Constrained Sequence

Medium

ArrayGreedyGreedyDynamic Programming

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

Use a greedy approach to propagate values from left to right and right to left, respecting both the diff and restrictions to maximize the sequence's largest value.

⚙️

Algorithm

3 steps

1Step 1: Initialize an array 'a' with zeros and apply restrictions to set upper bounds.
2Step 2: Traverse from left to right, updating a[i] = min(a[i-1] + diff[i-1], a[i]) to respect the diff constraints.
3Step 3: Traverse from right to left, updating a[i] = min(a[i+1] + diff[i], a[i]) to ensure all constraints are satisfied.

solution.py9 lines

1def maxValue(n, restrictions, diff):
2    a = [0] * n
3    for idx, maxVal in restrictions:
4        a[idx] = maxVal
5    for i in range(1, n):
6        a[i] = min(a[i], a[i-1] + diff[i-1])
7    for i in range(n-2, -1, -1):
8        a[i] = min(a[i], a[i+1] + diff[i])
9    return max(a)

ℹ

Complexity note: The linear complexity comes from two passes through the array to enforce constraints, making it efficient.

1Restrictions limit values directly and indirectly.
2Greedy propagation ensures maximum values are achieved while respecting constraints.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.