Big-O Complexity Flashcards
34 Karten · EN → EN · Coding
Master algorithm time and space complexity analysis. Covers all major complexity classes, common algorithm complexities, data structure operation costs, and key rules for calculating Big-O. Essential for coding interviews and writing efficient software.
This page covers Big-O Complexity: 34 flashcards in the coding category. Cards are oriented around the EN → EN language pair, so each prompt trains recognition in the direction you actually need. Difficulty is tagged intermediate, so you can expect a intermediate-friendly progression rather than a random dump of trivia.
FSRS keeps interview and engineering flashcards efficient: stable HTTP or complexity facts drift further out, while shaky ones return sooner. That is how you keep all 34 prompts in "Big-O Complexity" cold-start ready without grinding the whole list every night.
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Kartenvorschau
Die ersten 20 Karten aus diesem Deck (Vorderseite / Rückseite).
| # | Vorderseite | Rückseite |
|---|---|---|
| 1 | What is Big-O notation? | A mathematical notation describing the upper bound (worst-case) growth rate of an algorithm's time or space requirements as input size n grows toward infinity. It ignores constants and low-order terms. |
| 2 | O(1) — Constant Time | Execution time does NOT grow with input size. Single operation regardless of n. The best possible complexity. |
| 3 | O(log n) — Logarithmic Time | Time grows proportional to log₂(n). Each step halves (or divides) the problem. Doubling input adds only ONE more step. Excellent for large datasets. |
| 4 | O(n) — Linear Time | Time grows directly proportional to input size. If n doubles, time doubles. Typically means visiting each element once. |
| 5 | O(n log n) — Linearithmic Time | Slightly worse than linear. Common in divide-and-conquer sorting algorithms. Often considered the theoretical minimum for comparison-based sorting. |
| 6 | O(n²) — Quadratic Time | Time grows as the square of input size. Doubling n quadruples the time. Usually appears with nested loops over the same data. |
| 7 | O(n³) — Cubic Time | Time grows as the cube of input size. Typically three nested loops. Practical only for very small inputs. |
| 8 | O(2ⁿ) — Exponential Time | Time doubles with each additional input element. Quickly becomes infeasible even for moderate n. Often arises in brute-force recursive algorithms. |
| 9 | O(n!) — Factorial Time | Time grows factorially with input size. Even n=20 produces 2.4 quintillion operations. Only feasible for tiny inputs (n < ~12). |
| 10 | Big-O Complexity Order (best to worst) | O(1) < O(log n) < O(√n) < O(n) < O(n log n) < O(n²) < O(n³) < O(2ⁿ) < O(n!) |
| 11 | Rule: Drop Constants | Big-O ignores constant multipliers. O(2n), O(100n), and O(0.001n) are all O(n). Constants don't affect the growth class. |
| 12 | Rule: Drop Non-Dominant Terms | Keep only the fastest-growing term. O(n² + n) = O(n²). O(2ⁿ + n³) = O(2ⁿ). |
| 13 | Rule: Different Variables for Different Inputs | If an algorithm processes two independent inputs of sizes n and m, the complexity is O(n + m) or O(n × m), NOT O(n²). |
| 14 | Space Complexity | Amount of extra memory an algorithm uses relative to input size n. Measured the same way as time complexity. Input itself doesn't count (only auxiliary space). |
| 15 | Array — Access | O(1) — Direct index access |
| 16 | Array — Search (unsorted) | O(n) — Must check every element in worst case |
| 17 | Array — Insert/Delete (end) | O(1) amortized for dynamic arrays (e.g., push/pop at end) |
| 18 | Array — Insert/Delete (middle) | O(n) — Must shift elements to fill the gap or make room |
| 19 | Hash Map — Lookup / Insert / Delete | O(1) average case. O(n) worst case (all keys collide — extremely rare with good hash functions). |
| 20 | Binary Search Tree (balanced) — Search/Insert/Delete | O(log n) for balanced BST (AVL, Red-Black). O(n) worst case for degenerate (unbalanced) BST. |
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Häufig gestellte Fragen
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How many Big-O Complexity cards are in this deck?
This deck includes 34 cards. The preview on this page shows the first 20 so you can judge style and difficulty before cloning.
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