This repository documents a series of small experiments and notes focused on understanding how programs act/perform as the input data gets bigger and bigger. Data Structures and Algorithms
In academic settings, Data Structures and Algorithms are often studied using simplified inputs:
- Small datasets
- Single test cases
- Emphasis on correctness over scale
- Limited visibility into performance behavior
As input size increases, algorithms exhibit distinct growth patterns:
- Execution time scales at different rates
- Memory consumption becomes significant
- Certain solutions degrade faster than others
- Data structure choice directly impacts feasibility
This repository contains a collection of completed DSA assignments that examine how programs behave as input data grows.
Each implementation demonstrates not only correctness, but also:
- Time and space complexity in practice
- Observable performance differences between approaches
- Scalability limits of common algorithms
- The relationship between theoretical analysis and real execution
The goal is to reinforce core DSA concepts by showing how algorithmic design decisions affect behavior at scale.
- Understand text based problem description
- implement/answer the problem using the easiest tool (for me it was python)
- implement a similar structure to the desired language/syntax
/
├── as1/
│ ├── a.exe
│ └── as1.c
│ └── as1.py
│ └── context.txt
│ └── analysis.txt