Developed and Optimized Python Algorithms for Multiple Problems

2 min read

Developed and Optimized Python Algorithms for Multiple Problems

  • Day: 2023-09-07
  • Time: 23:20 to 23:40
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Python, Algorithms, Optimization, Error Correction, Greedy Algorithm

Description

Session Goal

The goal of this session was to develop and optimize several Python algorithms, addressing specific computational problems and exploring different algorithmic approaches.

Key Activities

  • Implemented the min_descontento function in Python to calculate total discontentment by optimizing the order of correction based on times and coefficients.
  • Corrected an error in the min_descontento function related to variable usage, ensuring accurate computation of discontentment.
  • Developed the saldos_sospechosos function in Python to determine the nature of numbers in relation to a final balance, indicating if they should be positive, negative, or ambivalent.
  • Explored iterative and greedy approaches to the ‘Suspicious Balances’ problem, comparing their efficiency and limitations with dynamic programming and backtracking methods.
  • Designed a segmentation strategy for choripán vendor distribution, implementing a Python solution to divide stalls into segments and place vendors at midpoints.
  • Analyzed a greedy algorithm approach for the choripán problem, discussing its efficiency and comparing it with other algorithmic strategies.

Achievements

  • Successfully implemented and corrected multiple Python functions to solve specific algorithmic problems.
  • Explored and documented various algorithmic strategies, enhancing understanding of their efficiency and limitations.

Pending Tasks

  • Further exploration of dynamic programming and backtracking methods for the ‘Suspicious Balances’ and choripán problems to identify optimal solutions.

Evidence

  • source_file=2023-09-07.sessions.jsonl, line_number=3, event_count=0, session_id=0b1de06ad6531f13e1aaa46f01c1af2b7daf46279f10644cf1a5d93f60b91a0f
  • event_ids: []

Graph View

Backlinks