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My first instinct was binary search and that was wrong.
Treat the problem as finding the minimum of a convex function using ternary search, since the vertex is the unique extremum. Clarify assumptions about the function's form (e.g., upward-opening parabola) and the range's validity, then implement ternary search with a precision threshold.
Pro tip: Mention that ternary search is essentially binary search on the derivative, and discuss how to handle integer vs. floating-point precision to avoid infinite loops.
Ask whether the quadratic opens upward or downward, and whether the vertex is a minimum or maximum. Confirm the range is guaranteed to contain the vertex and discuss precision requirements.
Select ternary search because it efficiently finds the extremum of a unimodal function. Explain that it works by comparing function values at two interior points and discarding one-third of the range each iteration.
Write pseudocode: while (right - left > epsilon), compute m1 = left + (right-left)/3, m2 = right - (right-left)/3. If f(m1) < f(m2), set right = m2; else set left = m1. Return (left+right)/2.
State that time complexity is O(log(1/epsilon)) and space is O(1). Compare with binary search on derivative (if derivative available) and discuss when each is preferable.
Discuss precision issues, integer overflow, and termination conditions. Mention that if the function is not strictly unimodal, ternary search may fail.
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