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## No, The TSP Isn't NP Complete
Two recent blog posts discussing the Traveling Saleman Problem (TSP) led me to write this post. The two blog posts are What is Operations Research by Graham Kendall, and I’ve Been Everywhere (Optimally…) by Rob Jefferson. Both are worth reading (I wish I had written them..). These posts share two interesting properties: both discuss the TSP, and both make a slight mistake about the TSP. The same mistake occurs regularly in blog posts and even books. The mistake is... [More]
Tags: np analytics optimization |

## Machine Learning As Prescriptive Analytics
I made a mistake about machine learning. Repeatedly. I said, and I wrote, that machine learning and predictive analytics were almost the same. To be more specific, my view was simple: analytics can be divided in four categories, exemplified below (see Analytics Landscape for details) I put machine learning near predictive analytics in this 2D landscape: Of course, I also put optimization as the queen of all analytics technologies as it yields best business value. What else would you expect from someone who spent nearly 3... [More]
Tags: machine_learning optimization analytics |

## NP Or Not NP? That Is The Question
A recent blog entry on TSP and NP completeness made me write the long overdue entry I wanted to write about complexity of optimization problems. It comes in play when customers ask this simple question: My problem takes too long to solve, what can I do? I'm pretty sure most optimization professionals heard this question at least once. I already blogged about it in my It Is Too Slow entry without actually answering it (clever isn't it?) Here are various ways to answer it depending on your own agenda. As
an employee of one of the largest... [More]
Tags: analytics optimization complexity |

## We must show the pain before we can propose the cure
Part of my job is to inject optimization in IBM Anaytics solutions. During one of the discussions with solution teams we argued about a fairly general issue that can prevent prescriptive analytics adoption. I think it is worth sharing. Specifically, one colleague presented the following analytics classification. I said that we should rather use the one below (I discussed it in Prescriptive vs Predictive Analytics Explained .) where the question prescriptive analytics answers is " What should I do about it?... [More]
Tags: analytics optimization |

## A Nice Optimization Problem From Santa Claus
Kaggle is a site that is most known for hosting machine learning competitions. However, once a year, Kaggle team runs an optimization competition on some problem Santa Claus could face. This year competition is a stochastic optimization problem: we are asked to optimize some outcome when the data is known with some uncertainty. Many real word problems are of this form. For instance, optimizing store replenishment and inventory levels takes as input sales forecasts. By definition, future sales are only known up to some... [More]
Tags: analytics optimization |

## Prescriptive Analytics Is Easier And More Profitable Than Predictive Analytics
When you hear about algorithms these days, chances are that you hear about machine learning or predictive analytics. (Some make a distinction between machine learning and predictive analytics, but the distinction is not material for this post. I'll use both interchangeably here). A quick search returns recent discussion in the news of machine learning algorithms: Using Algorithms to Determine Character , When Algorithms Discriminate ,... [More]
Tags: optimization analytics predictive prescriptive |

## Computing The Really Optimal Tour Across The USA On The Cloud With Python
When Randy Olson's Computing the optimal road trip across the U.S. resulted in articles in the Washington Post , NY Daily News , Daily Mail , People Magazine , NY Times , NPR , and many other outlets, the mathematical optimization community got surprised, and almost shocked. It got surprised for a couple of reasons. First reason to be surprised, the road trip computed by Randy Olson was not optimal, i.e. there is a shorter tour. The first to publish the shorter tour was Bill Cook in... [More]
Tags: optimization cloud analytics python |

## Actionable Insights
It is good practice to eat your own food. I should be no exception. In my post on the role of data science I was blaming data scientists who left business users without any clue about how to use the insights they produce. I should do the same, and help businesses use the advice I gave in that post: Data science role is to enable data based decision making. What does it mean in practice for a business? It means that data scientists should not only provide interesting insights, but they also should care... [More]
Tags: decision big_data data_science analytics optimization |

## Solving the hardest Sudoku - part 1
Do you know the hardest Sudoku problem? Do you know the best way to solve it? Before answering these questions, let me remind you of what the Sudoku puzzle is about in case you haven't read a newspaper in the last decade (adapted from wikipedia ): The objective is to fill a 9×9 grid with digits so that the digits in each column, each row, and each of the nine 3×3 sub-grids that compose the grid (also called ""blocks") are pairwise different. The puzzle setter provides a partially completed grid, which... [More]
Tags: sudoku mathematical_optimization analytics optimization mathematics constraint_programming |

## Proactive Analytics
Why blog again about optimization and analytics? Because the current way of having optimization be part of analytics is a bit misleading. Let me first say I assume that optimization is part of analytics here. Granted, a previous post of mine supported a different view, but the idea that mathematical optimization is part of the broader category of analytics is gaining momentum. For instance, the INFORMS society is pushing for it with its... [More]
Tags: analytics optimization |

## Issues Are Not Where One Think They Are
Where are the issues when one tries to use optimization to improve business? They may not lie where one think. My former colleague Laurent Perron (now at Google) splits the average time spent on optimization projects as follow in his CP 2011 invited talk : 50% Getting the right problem with the right people 25% Getting clean data 5% Solving the problem 20% Reporting the results/Explaining the implications One could argue about the exact split, but the broad picture is true as far as I can tell from my experience. I would... [More]
Tags: graphics analytics optimization |

## What Is The Solution When There Is No Solution ?
Optimization is like a Ferarri, when you drive it correctly you can
achieve incredible performance . But you must understand what it can
do and what it can't do or you will crash. Same is true for optimization. I'm starting a series of posts on various
pitfalls that people using optimization can fall into. This is the what it can't do part . This will complement posts where I brag about the value of optimization, which are centered around what it can do . Today's topic is about the difference between an exact answer , and a useful... [More]
Tags: overconstrained infeasibility modeling optimization |

## Discovering IBM
I just joined IBM as an employee of the former ILOG company that IBM just acquired. Within ILOG I was in charge of the development of ILOG mathematical optimization products (CPLEX, CP Optimizer, OPL, and ODM). I will continue in this role within the AIM division of IBM Software Group. Joining IBM is very exciting because IBM has a long story in the mathematical optimization field. Currently IBM has a strong group in IBM Research. It also has just launched a new initiative in its Global Business Service division, namely BAO (Business... [More]
Tags: optimization ilog |

## Perception Matters
I lenjoyed reading the following from Dear Mona, Which Is The Fastest Check-Out Lane At The Grocery Store? (You should read it all, as it provides an interesting crash course on queuing theory in practice): After airline passengers wouldn’t stop complaining about the time they spent at baggage claim (even when more staff were added and wait times fell) a Houston airport simply moved the arrival gates so that passengers spent more of their “wait” time walking to... [More]
Tags: optimization psychology analytics |

## Predicting Cyclist Speed
I have been the 'data scientist' on the IBM team that helped Dave Haase run the Race Across America (RAAM) this year. This project exemplified quite a few of the classical tips of data science documents in The Inconvenient Truth About Data Science : Data is never clean. You will spend most of your time cleaning and preparing data. 95% of tasks do not require deep learning. In 90% of cases generalized linear regression will do the trick. Big Data is just a tool. You should embrace the Bayesian... [More]
Tags: optimization python analytics data_science |