Applied Metaphors: Learning TRIZ, Complexity, Data/Stats/ML using Metaphors
  1. Teaching
  2. Data Viz and Analytics
  3. Prescriptive Modelling
  4. πŸ“ Intro to Linear Programming
  • Teaching
    • Data Viz and Analytics
      • Tools
        • Introduction to R and RStudio
        • Introduction to Radiant
        • Introduction to Orange
      • Descriptive Analytics
        • Data
        • Graphs
        • Summaries
        • Counts
        • Quantities
        • Groups
        • Densities
        • Groups and Densities
        • Change
        • Proportions
        • Parts of a Whole
        • Evolution and Flow
        • Ratings and Rankings
        • Surveys
        • Time
        • Space
        • Networks
        • Experiments
        • Miscellaneous Graphing Tools, and References
      • Statistical Inference
        • 🧭 Basics of Statistical Inference
        • 🎲 Samples, Populations, Statistics and Inference
        • Basics of Randomization Tests
        • πŸƒ Inference for a Single Mean
        • πŸƒ Inference for Two Independent Means
        • πŸƒ Inference for Comparing Two Paired Means
        • Comparing Multiple Means with ANOVA
        • Inference for Correlation
        • πŸƒ Testing a Single Proportion
        • πŸƒ Inference Test for Two Proportions
      • Inferential Modelling
        • Modelling with Linear Regression
        • Modelling with Logistic Regression
        • πŸ•” Modelling and Predicting Time Series
      • Predictive Modelling
        • πŸ‰ Intro to Orange
        • ML - Regression
        • ML - Classification
        • ML - Clustering
      • Prescriptive Modelling
        • πŸ“ Intro to Linear Programming
        • πŸ’­ The Simplex Method - Intuitively
        • πŸ“… The Simplex Method - In Excel
      • Workflow
        • Facing the Abyss
        • I Publish, therefore I Am
      • Using AI in Analytics
        • Case Studies
          • Demo:Product Packaging and Elderly People
          • Ikea Furniture
          • Movie Profits
          • Gender at the Work Place
          • Heptathlon
          • School Scores
          • Children's Games
          • Valentine’s Day Spending
          • Women Live Longer?
          • Hearing Loss in Children
          • California Transit Payments
          • Seaweed Nutrients
          • Coffee Flavours
          • Legionnaire’s Disease in the USA
          • Antarctic Sea ice
          • William Farr's Observations on Cholera in London
      • TRIZ for Problem Solvers
        • I am Water
        • I am What I yam
        • Birds of Different Feathers
        • I Connect therefore I am
        • I Think, Therefore I am
        • The Art of Parallel Thinking
        • A Year of Metaphoric Thinking
        • TRIZ - Problems and Contradictions
        • TRIZ - The Unreasonable Effectiveness of Available Resources
        • TRIZ - The Ideal Final Result
        • TRIZ - A Contradictory Language
        • TRIZ - The Contradiction Matrix Workflow
        • TRIZ - The Laws of Evolution
        • TRIZ - Substance Field Analysis, and ARIZ
      • Math Models for Creative Coders
        • Maths Basics
          • Vectors
          • Matrix Algebra Whirlwind Tour
          • content/courses/MathModelsDesign/Modules/05-Maths/70-MultiDimensionGeometry/index.qmd
        • Tech
          • Tools and Installation
          • Adding Libraries to p5.js
          • Using Constructor Objects in p5.js
        • Geometry
          • Circles
          • Complex Numbers
          • Fractals
          • Affine Transformation Fractals
          • L-Systems
          • Kolams and Lusona
        • Media
          • Fourier Series
          • Additive Sound Synthesis
          • Making Noise Predictably
          • The Karplus-Strong Guitar Algorithm
        • AI
          • Working with Neural Nets
          • The Perceptron
          • The Multilayer Perceptron
          • MLPs and Backpropagation
          • Gradient Descent
        • Projects
          • Projects
      • Tech for Creative Education
        • 🧭 Using Idyll
        • 🧭 Using Apparatus
        • 🧭 Using g9.js
      • Literary Jukebox: In Short, the World
        • Italy - Dino Buzzati
        • France - Guy de Maupassant
        • Japan - Hisaye Yamamoto
        • Peru - Ventura Garcia Calderon
        • Russia - Maxim Gorky
        • Egypt - Alifa Rifaat
        • Brazil - Clarice Lispector
        • England - V S Pritchett
        • Russia - Ivan Bunin
        • Czechia - Milan Kundera
        • Sweden - Lars Gustaffsson
        • Canada - John Cheever
        • Ireland - William Trevor
        • USA - Raymond Carver
        • Italy - Primo Levi
        • India - Ruth Prawer Jhabvala
        • USA - Carson McCullers
        • Zimbabwe - Petina Gappah
        • India - Bharati Mukherjee
        • USA - Lucia Berlin
        • USA - Grace Paley
        • England - Angela Carter
        • USA - Kurt Vonnegut
        • Spain-Merce Rodoreda
        • Israel - Ruth Calderon
        • Israel - Etgar Keret
    • Posts
    • Blogs and Talks

    On this page

    • Introduction
    • Demonstration of Level Curve
    • Linear Programming Solver
    • Linear Programming in 3D view
    • Linear Programming Interactive
    • References
    1. Teaching
    2. Data Viz and Analytics
    3. Prescriptive Modelling
    4. πŸ“ Intro to Linear Programming

    πŸ“ Intro to Linear Programming

    Author

    Arvind Venkatadri

    Published

    November 10, 2022

    Modified

    May 21, 2024

    library(blogdown)
    library(gMOIP)
    # See: https://relund.github.io/gMOIP/index.html
    library(knitr)
    library(rgl)
    rgl::setupKnitr()
    options(rgl.useNULL=TRUE)
    opts_chunk$set(
      echo = FALSE,
      collapse = TRUE,
      #cache = TRUE, autodep = TRUE, 
      comment = "#>",
      fig.show = "asis", 
      warning=FALSE, message=FALSE, include = TRUE, 
      out.width = "99%", fig.width = 8, fig.align = "center", fig.asp = 0.62
    )

    Introduction

    What is Linear Programming?

    Demonstration of Level Curve

    Linear Programming Solver

    Linear Programming in 3D view

    Linear Programming Interactive

    Let us say we have a Linear Programming problem with 3 variables: We define the model:

    Maximise:20x1+10x2+15x3Subject tox1+x2+x3<=103x1+x3<=24

    Here is the interactive LP Polytope:

    References

    1. Virginia Postrel, Operations Everything, Boston Globe, Hune 27, 2004. http://archive.boston.com/news/globe/ideas/articles/2004/06/27/operation_everything?pg=full
    Back to top
    Prescriptive Modelling
    πŸ’­ The Simplex Method - Intuitively

    License: CC BY-SA 2.0

    Website made with ❀️ and Quarto, by Arvind V.

    Hosted by Netlify .