Big O notation, common sorting and searching algorithms, and recursion patterns tested in technical interviews and coursework.
38 cards · basic cards · AI-written, checked twice. Edit anything.
- What does Big O notation measure?
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How the runtime or space requirements of an algorithm grow as input size increases.- Define O(1) time complexity.
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Constant time; the runtime does not change regardless of input size.- Define O(n) time complexity.
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Linear time; runtime grows proportionally with input size n.- Define O(log n) time complexity.
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Logarithmic time; runtime grows with the logarithm of input size.- Define O(n^2) time complexity.
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Quadratic time; runtime grows with the square of input size.- Define O(n log n) time complexity.
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Linearithmic time; runtime is n times the logarithm of n.- Define O(2^n) time complexity.
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Exponential time; runtime doubles with each increase in input size.- What is the time complexity of accessing an array element by index?
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O(1) constant time.- What is the time complexity of searching an unsorted array for a value?
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O(n) linear time.- What is the time complexity of binary search on a sorted array?
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O(log n) logarithmic time.- What must be true about an array before binary search can be used?
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The array must be sorted.- What is the time complexity of inserting an element at the beginning of an array?
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O(n) linear time.- What is the time complexity of appending an element to a dynamic array with available capacity?
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O(1) amortized constant time.- What is the average time complexity of searching a hash table?
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O(1) constant time with a good hash function.- What is the time complexity of quicksort in the average case?
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O(n log n) linearithmic time.
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