Algorithms Research & Evidence-Based Practice 5

Question 1

What is the 'no free lunch theorem' and its implication for algorithm research?

Accepted Answer

No single algorithm outperforms all others across every possible input distribution

Answer

The no free lunch theorem states that no single algorithm dominates all others across all problem instances, emphasizing that benchmarks must cover diverse inputs.

Question 2

In the context of algorithm research, what is 'construct validity'?

Accepted Answer

Whether the metrics chosen (e.g., wall-clock time) accurately reflect the theoretical concept being measured (e.g., efficiency)

Answer

Construct validity asks whether the empirical measure (e.g., runtime in milliseconds) genuinely captures the theoretical construct (e.g., computational efficiency) under study.

Question 3

Why should algorithm researchers use open benchmark suites (e.g., SNAP for graphs) instead of custom private datasets when possible?

Accepted Answer

Open benchmarks enable reproducibility and fair comparison with prior work

Answer

Standardized open benchmarks let different research groups run the same inputs, making results directly comparable and experiments reproducible.

Question 4

A new approximation algorithm for TSP claims a 1.5-approximation ratio. What empirical evidence would best complement this theoretical guarantee?

Accepted Answer

Benchmark comparisons showing the algorithm's solution quality and runtime on diverse TSP instances versus existing methods

Answer

Empirical benchmarks show how the approximation guarantee manifests in practice and whether the algorithm is competitive with existing approaches on real instances.

Question 5

What is 'confirmation bias' in algorithm research and how can it be mitigated?

Accepted Answer

The tendency to design experiments that favor a hypothesis, mitigated by pre-registering experimental protocols

Answer

Confirmation bias in experiments involves unconsciously designing setups that favor the expected result; pre-registration commits to methodology before seeing outcomes.

Algorithms Practice Test

Algorithms Practice Test

Algorithms Research & Evidence-Based Practice 5