Intro to Optimization Through the Lens of Data Science Pt. 3

Name: Intro to Optimization Through the Lens of Data Science Pt. 3
Rating: 4.9 (105 reviews)

A comprehensive introduction to mathematical optimization and gurobipy tailored to data scientists and problem solvers

Created byDr. Joel Sokol

Last updated 4/2024

English

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What you'll learn

What is optimization and how can it be applied to complex problems?
How to identify an optimization problem and translate real life into optimization models
Learn about solvers and algorithms
Introduction to Gurobi/gurobipy and using it in exercises and real-world problem solving

Course content

2 sections • 13 lectures • 1h 50m total length

Fixed Costs, Linking to Continuous Variables8:07
Explore binary variables to model fixed costs and yes/no investments, link them to amounts under a budget, balance return and risk with theta, and include transaction costs and indicator constraints.
Yes/No Decisions and Related Constraints9:19
Explore yes/no decisions and binary variables in optimization by modeling investment choices, minimum spends, energy stock constraints, and if-then relationships.
Using Truth Tables to Troubleshoot Constraints4:54
Explore how to use truth tables to validate constraints in binary-variable optimization models, ensuring the mathematical constraint matches the intended English logic and catching mis-specifications.
Modeling Logic with Binary Variables9:54
Explore modeling logic with binary variables using truth tables and constraints such as and, or, xor. Learn about the opposite constraint, the different constraint, if-then, contrapositive, and gurobi pi representations.
Building Complex Logical Constraints8:17
Learn to model complex logical constraints in binary optimization by building from simpler parts, using either‑or logic, truth tables, half weights, and multiple constraints.
Example: Rolling-Horizon Power Generation10:06
Explore a rolling-horizon optimization model for electric power generation, using integer and binary variables to minimize generation cost while enforcing monthly emissions and daily limits with fines.

Knapsack and Covering Models10:03
Explore knapsack and covering archetypes in optimization, using binary variables and capacity constraints. Use set cover with minimum cost to cover data science topics including regression and random forests.
Blending Model10:28
The blending model links raw materials to finished products by blending attributes using linear constraints and nonnegativity to maximize revenue minus costs in data driven optimization.
Cutting Stock and Bin Packing Models9:35
Explore the cutting stock model, using patterns to cut large rolls into smaller ones to meet demand with minimal scrap; learn its relation to bin packing and column generation.
Network Model12:11
Explore network models in optimization, using nodes and directed arcs to model supplies, demands, and costs; formulate balance and capacity constraints to minimize shipping cost, noting that integer solutions arise.
Shortest Path Model5:46
Explore the shortest path model in a network of nodes and arcs, where supply, demand, and arc costs yield the fastest route by minimizing travel time.
Assignment Model7:54
Explore the assignment model as a specialized network flow problem, where each person supplies one unit and each project demands one, maximizing total value while enforcing capacity and integrality constraints.
Modeling Note: Dealing with Roundoff Error4:22
Explore how floating point roundoff misclassifies 0–1 variables in Gurobi optimization and why simple equality fails. Use a tolerance epsilon to identify near-one values for both continuous and discrete variables.

Requirements

Basic Python, college-level mathematics, experience with Jupyter Notebooks, and the ability to open python (.py) and Jupyter Notebooks (.ipynb) files on your machine

Description

Welcome to Introduction to Optimization Through the Lens of Data Science!

This free 4-part course was developed to help teach data scientists how to add optimization to their toolbox and when to use it in their advanced problem-solving. We will cover a comprehensive introduction to optimization, when optimization is the best tool to solve a problem, and how to translate real-life problems into optimization.

We will introduce you to world-class tools to help you problem solve, and provide everything from basic hands-on exercises to more advanced full real-world use cases to reinforce all new concepts of prescriptive analytics as you learn them. We look forward to having you learn optimization (and gurobipy) with expertise from Dr. Joel Sokol and the team of Ph.D. experts from Gurobi Optimization, who helped develop this comprehensive introduction to mathematical optimization.

In part 3, you will model yes/no decisions and complex logical constraints with binary variables and link them to continuous variables. You will also explore classic optimization model archetypes.

Hands-on Exercises:

Please check the resource section of many of the lectures to find self-assessments in the form of exercise files and solution files. You will also notice we have data and code files available to help you work your way through these practice exercises.

Who this course is for:

Data scientists and problem solvers curious about mathematical optimization/prescriptive analytics.

Intro to Optimization Through the Lens of Data Science Pt. 3

What you'll learn

Explore related topics

Course content

Adding Complexity: Binary Variables, Constraints, and Troubleshooting6 lectures • 51min

The Archetypes: Learning Classic Optimization Models7 lectures • 1hr

Requirements

Description

Who this course is for: