Mixed Integer Programming

In our enterprise, Mixed integer Programming (MIP) was one of the most widely used and powerful techniques, long before AI.   But there are lots of tricks to make it really applicable.   Its still very useful.   Here a useful introduction with links to implementation systems.  Also makes a very good point about understanding how the structure of the problem is important.  And efficiency is crucial.  Also this is one space where maturing quantum methods are likely to become very useful.  By necessity is technical.  

A comprehensive study of Mixed Integer Programming with JuMP on Julia (Part 1)

Some basics of Linear/Mixed Integer Programming & How to use a heuristic callback inside a MIP solver.

By Ouaguenouni Mohamed  in TowardsDataScience

Introduction

One of the primary purposes of the computer sciences and operation research is to solve problems efficiently; problem-solving is a field where we often find very “ad-hoc” methods of resolution, they can be efficient, but they rely on some specific properties of the problem which are not necessarily easy to notice.

In this series of posts, we will introduce and discover a very versatile and generic way of thinking and of solving a wide variety of problems, and this introduction will occur on three sides:.... ' 

Webinar: Mixed Integer Programming plus Machine Learning

Highly recommended!   We used Mixed Integer Programming optimization methods for many years in the enterprise.  Used it every day to solve tough problems.  It was our horse for optimization problems.   Especially for complex combinatorial decisions.  Nice to see this upcoming Webinar as presenting it as complimentary to Machine Learning (ML).   Then ML can be used to directly make key resulting decisions!   Always better to get closer to the decision.  I will be attending.

Title: Mathematical Optimization + ML: Featuring Forrester Survey Insights
Date: 9/17/2019
Time: 9:00 AM PDT
Duration: 60 Minutes

Overview:
Mathematical optimization (AKA Mixed Integer Programming) and Machine Learning (ML) are different but complementary technologies. Simply put – Mixed Integer Programming (MIP) answers questions that ML cannot. Machine learning makes predictions while MIP makes decisions. For Data Scientists to be effective, an understanding of MIP and when to use it is critical, as ML does not solve all problems effectively.

In this latest Data Science Central webinar, you will hear the results of the 2019 Mathematical Optimization Survey commissioned by Gurobi and conducted by Forrester and insights on how Data Scientists can use tools such as MIP to make complex decisions.

You’ll learn:
- The latest trends in ML and Artificial Intelligence
- Key findings from the Mathematical Optimization Survey
- How you can use MIP in concert with ML techniques
- How industries are using MIP today to efficiently use resources, often resulting in time savings and millions of dollars in cost savings

All registrants will receive a copy of the 2019 Mathematical Optimization Study available in October 2019.

Speakers: 
Mike Gualtieri, VP Principal Analyst, Application Development and Delivery Professionals - Forrester Research
Edward Rothberg, CEO and Co-Founder - Gurobi Optimization

Hosted by: 
Stephanie Glen, Editorial Director - Data Science Central
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