Python - Design and Analysis of Algorithms

This repository is a continuation of my previous project on fundamental algorithms and data structures. While the earlier repo focused on core algorithmic concepts, this one dives deeper into advanced topics in the design and analysis of algorithms using Python.

Overview

The repository is organized into modules, each dedicated to a key area of algorithm development and analysis. The tasks and solutions here are designed to help:

• Understand and analyze the quality of basic and advanced algorithms.
• Compare, justify, and organize data structures for specific tasks, considering their strengths, weaknesses, and interactions.
• Grasp recursion and apply recursive algorithms and methods to problem-solving.
• Evaluate the advantages and disadvantages of various algorithms and use them for practical tasks.
• Analyze, compare, and select the most effective algorithms for a given problem, considering their efficiency.

Topics Covered

Each topic explores essential aspects of algorithm design in different contexts:

• Divide and Conquer, Greedy Algorithms, and Dynamic Programming: Techniques for breaking down complex problems into simpler subproblems to achieve efficiency.
• Graphs, Trees, and Prefix Trees (Tries): Advanced data structures for optimization and search, including algorithms for working with networks, hierarchies, and large volumes of textual data.
• Big Data Algorithms and Parallel Computing: Adapting algorithms to the challenges of big data and multi-threaded environments.
• Optimization and Resource Management Algorithms: Algorithms for cache management, flow control, and optimizing systems with limited resources.
• Gradient Descent, Heuristics, and Simulated Annealing: Methods for solving optimization problems.
• Algorithmic Complexity, Approximation, and Randomized Algorithms: Tools for evaluating performance and tackling non-trivial problems.

Learning Outcomes

By working through this repository, you will:

• Develop algorithms for complex systems handling large data volumes (e.g., in cloud environments).
• Optimize software performance, crucial for critical systems.
• Analyze algorithm efficiency and adapt solutions to specific project needs.

Structure

The code is organized by topic in the src/ directory:

src/
  algo1/   # Divide and conquer, selection algorithms
  algo2/   # Greedy algorithms, dynamic programming
  algo3/   # Graphs, trees, and advanced data structures
  algo4/   # Prefix trees (tries) and string algorithms
  algo5/   # Big data algorithms: Bloom filter, HyperLogLog
  algo6/   # MapReduce, parallel and distributed algorithms
  algo7/   # Caching, optimization, and performance analysis
  algo8/   # Gradient descent, heuristics, and simulated annealing
  algo9/   # Algorithmic complexity, approximation, and randomized algorithms
  algo10/  # Special topics and advanced algorithmic techniques

Each module contains Python scripts with clear implementations and explanations.

Prerequisites

• Python 3.8+
• See individual requirements.txt files in each module for dependencies.

License

This project is for educational purposes.

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