What are common examiner traps in Algorithm Design Techniques?

Failing to re-sort or correctly manage the priority queue after merging composite nodes in Huffman coding, leading to incorrect tree depth. Confusing graph algorithms with different design strategies, such as mistaking Floyd-Warshall (DP) for a Greedy technique or Prim's (Greedy) for DP. Calculating only the average bit length per character (L) and forgetting to multiply by the total message length (N) in NAT questions. Classifying Quicksort or Mergesort as Dynamic Programming rather than Divide and Conquer due to recursive subproblem terminology. Missing standard transitions when shortcuts exist (e.g., forgetting the unit step to i+1 alongside the shortcut to j) Order of operations in loop iterations (squaring before the multiplication vs. after the condition check) Arithmetic errors when intermediate values become congruent to 0 mod n, causing all subsequent iterations to evaluate to 0 Incorrect dimension indexing when evaluating di-1 dk dj Omitting the recursive cost of sub-chains when evaluating a split point k Calculating only a specific greedy parenthesization instead of checking all split points k at each chain length Arithmetic errors when summing large scalar products (e.g., 10 × 100 × 80 vs 10 × 5 × 80)

GATE CSE Algorithm Design Techniques PYQs

Standard yield5.4% of Algorithms

GATE tests algorithm design techniques through two primary styles: conceptual classification and mechanical algorithmic execution. At the 1-mark level, question… Read the guide

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Questions: 6
Asked: 2011–2017
Total marks: 10
Marks / paper: 0.4

6 GATE CSE Algorithm Design Techniques PYQs with solutions, from Algorithms. Browse all GATE CSE PYQs.

Questions

Every official paper that asked this topic, newest first.

What GATE tests in Algorithm Design Techniques

Concepts, patterns, formulas and traps distilled from past papers.

GATE tests algorithm design techniques through two primary styles: conceptual classification and mechanical algorithmic execution. At the 1-mark level, questions focus on matching standard classical algorithms (such as MST algorithms, shortest paths, and sorting algorithms) to their corresponding paradigms like Greedy, Dynamic Programming, Divide and Conquer, or Backtracking. At the 2-mark level, the focus shifts to quantitative problem solving, such as building a Huffman tree and computing the exact total or expected bit length for encoded messages.

Core concepts

Greedy Paradigm (Kruskal's, Prim's, Huffman coding)
Divide and Conquer (Mergesort, Quicksort)
Dynamic Programming (Floyd-Warshall All-Pairs Shortest Path)
Backtracking (Hamiltonian Circuit search)
Huffman Tree Construction and Optimal Prefix Codes
Dynamic programming recurrence relations and state transitions
Optimal substructure in shortest-path / step-minimization problems
Repeated squaring / modular exponentiation algorithms
Pseudocode execution and state tracing
Matrix Chain Multiplication (MCM)
Dynamic Programming optimal substructure and overlapping subproblems
Bottom-up table filling by chain length
Optimal parenthesization evaluation

Recurring question patterns

Algorithm-to-Paradigm Matching
CommonMCQ1 marks2017, 2015
Matching a list of standard algorithms (e.g., Prim's, Kruskal's, Floyd-Warshall, Mergesort, Quicksort, Hamiltonian circuit) to their fundamental design technique (Greedy, DP, Divide and Conquer, Backtracking).
Huffman Coding Message Length Calculation
OccasionalNAT2 marks2017
Constructing an optimal prefix-free tree from character frequencies/probabilities to determine the codeword lengths and calculating the total or expected encoded message length in bits.
Recurrence / Optimal Substructure Formulation
OccasionalNAT2 marks2014
A shortest-path or dynamic programming problem on a discrete grid/line with special transitions (e.g., shortcuts) is specified. The question asks to identify the successor states in the Bellman optimality recurrence and compute an aggregate value (such as their product).
Pseudocode Tracing of Divide-and-Conquer / Arithmetic Algorithms
OccasionalNAT2 marks2015
A structured algorithm (such as left-to-right binary modular exponentiation using repeated squaring) is provided in pseudocode with concrete input parameters, requiring precise variable simulation and modulo arithmetic.
Matrix Chain Multiplication Optimal Cost Calculation
CommonMCQ2 marks2011
Given dimensions for a sequence of 4 matrices, compute the minimum number of scalar multiplications required to multiply them using dynamic programming recurrence relations.

Formulas GATE uses

Expected Length per Character in Huffman Coding

L = \sum_{i=1}^{k} p_i \cdot l_i

Used when calculating the average codeword length per character given character probabilities $p_i$ and tree depths/codeword lengths $l_i$ .

Total Encoded Message Length

\text{Total Bits} = N \times \sum_{i=1}^{k} p_i \cdot l_i

Used to find the total bit count of a message of $N$ characters encoded via variable-length prefix codes like Huffman coding.

Dynamic Programming Optimal Substructure Recurrence

T(i) = 1 + \min_{(i, j) \in E} T(j)

Used to express the minimal cost/steps to reach a destination from state $i$ by transitioning to reachable adjacent states $j$ .

Modular Exponentiation (Left-to-Right Binary Method)

z_{new} = ((z_{old}^2 \bmod n) \times a^{c[i]}) \bmod n

Used in repeated squaring algorithms to compute $a^b \bmod n$ where $b$ is represented in binary array $c$ .

Matrix Chain Multiplication DP Recurrence

m[i, j] = \begin{cases} 0 & \text{if } i = j \\ \min_{i \le k < j} \{ m[i, k] + m[k+1, j] + d_{i-1} d_k d_j \} & \text{if } i < j \end{cases}

Used to find the minimum number of scalar multiplications required to compute the product of matrices $M_i \dots M_j$ given dimension array $d$ .

Scalar Multiplications for Two Matrices

\text{Cost}(A_{p \times q} \times B_{q \times r}) = p \cdot q \cdot r

Used to compute the cost of multiplying an intermediate or single matrix of size $p \times q$ by another of size $q \times r$ .

Examiner traps in Algorithm Design Techniques

Failing to re-sort or correctly manage the priority queue after merging composite nodes in Huffman coding, leading to incorrect tree depth.
Confusing graph algorithms with different design strategies, such as mistaking Floyd-Warshall (DP) for a Greedy technique or Prim's (Greedy) for DP.
Calculating only the average bit length per character (L) and forgetting to multiply by the total message length (N) in NAT questions.
Classifying Quicksort or Mergesort as Dynamic Programming rather than Divide and Conquer due to recursive subproblem terminology.
Missing standard transitions when shortcuts exist (e.g., forgetting the unit step to i+1 alongside the shortcut to j)
Order of operations in loop iterations (squaring before the multiplication vs. after the condition check)
Arithmetic errors when intermediate values become congruent to 0 mod n, causing all subsequent iterations to evaluate to 0
Incorrect dimension indexing when evaluating di-1 dk dj
Omitting the recursive cost of sub-chains when evaluating a split point k
Calculating only a specific greedy parenthesization instead of checking all split points k at each chain length
Arithmetic errors when summing large scalar products (e.g., 10 × 100 × 80 vs 10 × 5 × 80)

How questions are asked

Match the following (Algorithm to Design Paradigm)
Compute total encoded bit length of a message via Huffman coding
Parameter evaluation in optimal recurrence relation
Execution trace and return value of pseudocode
Compute minimum scalar multiplications for given matrix dimensions

Paper trends

Direct 1-mark matching questions test quick recall of the design paradigms behind fundamental graph, sorting, and search algorithms.
2017, 2015
Shift toward NAT questions for greedy algorithmic computation (e.g., Huffman encoding) to evaluate numerical accuracy without multiple-choice elimination.
2017
Both questions appeared as 2-mark Numerical Answer Type (NAT) problems requiring exact numerical evaluation rather than asymptotic complexity derivations.
2015, 2014
Matrix chain multiplication is tested as a 2-mark calculation-heavy problem where candidates must apply bottom-up DP across chain lengths 2, 3, and 4.
2011

Difficulty notes

Easy: Direct paradigm matching of standard textbook algorithms (e.g., Prim's, Quicksort, Floyd-Warshall). Medium: Multi-step manual tracing of greedy algorithms (e.g., building a 5-symbol Huffman tree, finding depths, and evaluating message bit length). Hard: Complex recurrence relations, hybrid dynamic programming/greedy correctness proofs, or non-trivial state-space bounds.