πŸ“–
Wiki
CNCFSkywardAIHuggingFaceLinkedInKaggleMedium
  • Home
    • πŸš€About
  • πŸ‘©β€πŸ’»πŸ‘©Freesoftware
    • πŸ‰The GNU Hurd
      • πŸ˜„The files extension
      • πŸ“½οΈTutorial for starting
      • 🚚Continue Working for the Hurd
      • πŸš΄β€β™‚οΈcgo
        • πŸ‘―β€β™€οΈStatically VS Dynamically binding
        • 🧌Different ways in binding
        • πŸ‘¨β€πŸ’»Segfault
      • πŸ›ƒRust FFI
    • πŸ§šπŸ»β€β™‚οΈProgramming
      • πŸ“–Introduction to programming
      • πŸ“–Mutable Value Semantics
      • πŸ“–Linked List
      • πŸ“–Rust
        • πŸ“–Keyword dyn
        • πŸ“–Tonic framework
        • πŸ“–Tokio
        • πŸ“–Rust read files
  • πŸ›€οΈAI techniques
    • πŸ—„οΈframework
      • 🧷pytorch
      • πŸ““Time components
      • πŸ““burn
    • 🍑Adaptation
      • 🎁LoRA
        • ℹ️Matrix Factorization
        • πŸ“€SVD
          • ✝️Distillation of SVD
          • 🦎Eigenvalues of a covariance matrix
            • 🧧Eigenvalues
            • πŸͺCovariance Matrix
        • πŸ›«Checkpoint
      • 🎨PEFT
    • πŸ™‹β€β™‚οΈTraining
      • πŸ›»Training with QLoRA
      • 🦌Deep Speed
    • 🧠Stable Diffusion
      • πŸ€‘Stable Diffusion model
      • πŸ“ΌStable Diffusion v1 vs v2
      • πŸ€Όβ€β™€οΈThe important parameters for stunning AI image
      • ⚾Diffusion in image
      • 🚬Classifier Free Guidance
      • ⚜️Denoising strength
      • πŸ‘·Stable Diffusion workflow
      • πŸ“™LoRA(Stable Diffusion)
      • πŸ—ΊοΈDepth maps
      • πŸ“‹CLIP
      • βš•οΈEmbeddings
      • πŸ• VAE
      • πŸ’₯Conditioning
      • 🍁Diffusion sampling/samplers
      • πŸ₯ Prompt
      • πŸ˜„ControlNet
        • πŸͺ‘Settings Explained
        • 🐳ControlNet with models
    • πŸ¦™Large Language Model
      • ☺️SMID
      • πŸ‘¨β€πŸŒΎARM NEON
      • 🍊Metal
      • 🏁BLAS
      • πŸ‰ggml
      • πŸ’»llama.cpp
      • 🎞️Measuring model quality
      • πŸ₯žType for NNC
      • πŸ₯žToken
      • πŸ€Όβ€β™‚οΈDoc Retrieval && QA with LLMs
      • Hallucination(AI)
    • 🐹diffusers
      • πŸ’ͺDeconstruct the Stable Diffusion pipeline
  • 🎹Implementing
    • πŸ‘¨β€πŸ’»diffusers
      • πŸ“–The Annotated Diffusion Model
  • 🧩Trending
    • πŸ“–Trending
      • πŸ“–Vector database
      • 🍎Programming Languages
        • πŸ“–Go & Rust manage their memories
        • πŸ“–Performance of Rust and Python
        • πŸ“–Rust ownership and borrowing
      • πŸ“–Neural Network
        • 🎹Sliding window/convolutional filter
      • Quantum Machine Learning
  • 🎾Courses Collection
    • πŸ“–Courses Collection
      • πŸ“šAcademic In IT
        • πŸ“Reflective Writing
      • πŸ“–UCB
        • πŸ“–CS 61A
          • πŸ“–Computer Science
          • πŸ“–Scheme
          • πŸ“–Python
          • πŸ“–Data Abstraction
          • πŸ“–Object-Oriented Programming
          • πŸ“–Interpreters
          • πŸ“–Streams
      • 🍎MIT Algorithm Courses
        • 0️MIT 18.01
          • 0️Limits and continuity
          • 1️Derivatives
          • 3️Integrals
        • 1️MIT 6.042J
          • πŸ”’Number Theory
          • πŸ“ŠGraph Theory
            • 🌴Graph and Trees
            • 🌲Shortest Paths and Minimum Spanning Trees
        • 2️MIT 6.006
          • Intro and asymptotic notation
          • Sorting and Trees
            • Sorting
            • Trees
          • Hashing
          • Graphs
          • Shortest Paths
          • Dynamic Programming
          • Advanced
        • 3️MIT 6.046J
          • Divide and conquer
          • Dynamic programming
          • Greedy algorithms
          • Graph algorithms
Powered by GitBook
On this page

Was this helpful?

Edit on GitHub
  1. Courses Collection
  2. Courses Collection
  3. MIT Algorithm Courses
  4. MIT 6.046J

Greedy algorithms

PreviousDynamic programmingNextGraph algorithms

Last updated 1 year ago

Was this helpful?

A greedy algorithm is a simple heuristic approach that seels to fins the optimal solution to a problem by making locally optimal choices at each step. In other words, it makes the best possible decision at each step wihtou considering the overall problem, hoping that whis will lead to the best possible solution overall.

The key feature of a greedy algorithm is that it makes a choice that seems best at the moment, without worrying about future consequences or whether that choice will lead to an optimal solution. This can often be a useful approach, as it is generally fast ans easy to implement, but it can also be risky, as it may not always produce the best possible solution.

🎾
πŸ“–
🍎
3️
Activity selection for greedy algorithm