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FIT2004 Algorithms and data structures - S2 2026

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Consider a directed, weighted graph G with |V| vertices and |E| edges. What is the worst-case time complexity of running a Depth-First Search (DFS) on G, if G is implemented using an adjacency matrix?

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Consider a directed, weighted graph G with |V| vertices and |E| edges. What is the worst-case time complexity of listing all the outgoing edges of the vertex that has the most outgoing edges, if G is implemented using an adjacency matrix?

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Consider factorial(k)factorial(k): if k = 0k = 0 return 1, else return k \cdot factorial(k-1)k \cdot factorial(k-1). How many times is factorial invoked in total (counting the initial call and the base-case call) to compute factorial(5)factorial(5)?
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If |A| = 4|A| = 4 and |B| = 5|B| = 5, how many ordered pairs are in the Cartesian product A \times BA \times B?
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Which abstract data type (ADT) provides First-In-First-Out (FIFO) access?
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Find a closed-form solution for the following recurrence relation, and prove that it is correct:

T(n) = \begin{cases} 2T(n/2) + an, & \text{if } n > 1 \\ b, & \text{if } n = 1 \end{cases}T(n) = \begin{cases} 2T(n/2) + an, & \text{if } n > 1 \\ b, & \text{if } n = 1 \end{cases}

where a and b are positive constants. Assume n is a power of 2.

A complete answer contains three parts, and all three are marked:

  1. Working (2 marks) — unroll the recurrence level by level and show how the total is evaluated: how many levels there are, what each level contributes, and what the base cases contribute.
  2. Verification by induction (2 marks) — check your formula at n = 1, then show that if it is correct for n/2, the recurrence makes it correct for n.
  3. The closed form (2 marks) — stated on a single line at the bottom of your answer.

Type your answer as plain text: write powers as n^2, products as 4n or 4*n, and logarithms as log_x(n) for base x. You may draft on the paper provided, but only what you type into Moodle is marked.

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Consider the following algorithm, where A[1..n] is an array of integers with n \ge 1n \ge 1.

1 function ALG(A[1..n])

2 s = 0

3 for k = 1 to n do

4 # INVARIANT HOLDS HERE

5 if A[k] % 2 = 0 then

6 s = s + A[k]

7 return s

Prove that the following invariant holds every time execution reaches the line marked # INVARIANT HOLDS HERE — that is, at the start of the loop body, for each value of k the loop runs with:

s is the sum of the even elements of A[1..k-1]  (when k = 1, A[1..0] is empty, and an empty sum is 0)

A complete answer contains two parts, and both are marked:

  • Initialisation (2 marks) — the invariant is true the first time the marked line is reached, when k = 1.
  • Maintenance (4 marks) — assuming the invariant is true at the marked line for one value of k, show it then holds for k + 1 — and so is true at the next visit to the marked line, if the loop runs again. Consider what one pass of the loop body does in every case that can occur.

You do not need to prove the termination step, and you do not need to prove that the loop stops.

Type your answer as plain text: write ranges like A[1..k-1], and use ordinary words freely — full mathematical notation is not required. You may draft on the paper provided, but only what you type into Moodle is marked.

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Which of the following statements are true about the Median of Median algorithm?

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A fair coin is flipped 5050 times. What is the EXPECTED number of heads?
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Two INDEPENDENT events have probabilities 0.20.2 and 0.50.5. What is the probability that BOTH occur?
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