Algorithm Group 8
Algorithm Group 8 contains five exercises focused on multi-dimensional data analysis, range correction, dependency compatibility, generic combination generation, and condition-based Cartesian products.
Compared with earlier groups, these tasks introduce broader algorithmic problems where the implementation must often model relationships between multiple collections rather than process one collection independently.
Tasks
Task 1 — Unique and Common Values
Analyze multiple integer collections and identify:
- values that occur only once across the complete multi-dimensional collection
- values that are present in every input dimension
Task 2 — Sector Range Reconstruction
Process several integer collections assigned to numeric sectors.
Detect values placed in the wrong sector, move them to the correct sector, and determine which values are missing from each sector.
Task 3 — System Version Compatibility
Model three dependent systems and determine valid update, upgrade, and installation paths according to version-generation compatibility and dependency rules.
Task 4 — Generic Combination Generator
Create one generic function that produces the Cartesian product of an arbitrary number of input collections.
The function must work regardless of:
- the number of input lists
- the number of values inside each list
Task 5 — Conditional Combination Generator
Extend generic combination generation by:
- selecting specific source lists
- calculating the sum of each generated combination
- filtering combinations using a target value
- applying configurable delta rules
Objectives
The exercises in this group provide practice with:
- multi-dimensional collections
- global frequency analysis
- collection intersection
- numeric range validation
- data correction
- missing-value detection
- dependency graphs
- compatibility rules
- version relationships
- request and response modeling
- Cartesian products
- recursion or iterative combination generation
- generic algorithms
- configurable filtering
- delta parsing
- validation
General Design Considerations
Several exercises in this group benefit from separating the problem into stages.
For example:
Input
↓
Validation
↓
Normalization
↓
Processing
↓
Result Modeling
Trying to perform every operation inside one large loop may make the implementation harder to verify and reuse.
Generic Algorithms
Tasks 4 and 5 explicitly require a single abstract solution.
The implementation must not depend on a fixed number of source lists.
For example, the same generator should support:
2 lists
3 lists
5 lists
7 lists
without requiring a separate implementation for each case.
Implementation
The examples define the intended behavior for the provided inputs.
Where the original task contains an ambiguity or an inconsistent example, the individual task page documents the issue and defines a deterministic interpretation without changing the main objective of the exercise.