with Tags:
optimization
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## A Sudoku Web App Based On DOcloud And Python
In our previous posts ( here and here ) we have shown how to use Python and the DOcloud service for solving any Sudoku puzzle. This was nice but we had to manually modify our Python code each to time we wanted to solve a new grid. We present today a simple web app that lets user input hints on a grid and have it solved. It looks like this. We will start from our previous code . It used as input: A connection to DOcloud An OPL model for solving Sudoku An input string containing the hints of the grid we want to solve... [More]
Tags: cplex cloud analytics python optimization docloud opl |

## Solving Optimization Problems On The Cloud With Python
The availability of an api for solving optimization problems via DOcloud is opening new ways to develop an application: you can use the implementation language of your choice. Granted, at the time of writing, we only provide a client api for Java. But we also provide a REST api , which is what matters here. As an example, we present a very simple client api for Python that wraps the REST api. All you need to do is to provide as input a list of files that define the problem to be solved. These files can be created as... [More]
Tags: optimization analytics cplex cloud |

## Solving The Hardest Sudoku In Python With DOcloud
The more I use Python, the more I like it. Here is yet another example of why. I looked for Sudoku solvers written in Python and found quite a few. I particularly like the one Peter Norvig describes in Solving Every Sudoku Puzzle . As a matter of fact Peter wrote a constraint programming solver tailored to Sudoku. I recommend reading his blog entry if you want to learn Python. I also recommend it if you want to understand the basics of a constraint programming (CP) solver. Indeed, his CP... [More]
Tags: cloud optimization saas python analytics |

## Computing The Longest Tour Across The USA
My previous post Computing The Really Optimal Tour Across The USA On The Cloud With Python generated some interest (like all answer's to Randy Olson's original article ). One reader asked a seemingly simple question that got my interest: My first reaction was to ask what was the definition of a maximal tour? Indeed, without further information, we can get tour of arbitrary length by circling. For instance, let us consider two point only. What would be the longest route between those two points? Well,... [More]
Tags: python optimization analytics |

## 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 |

## Step By Step Modeling Of PuzzlOr Electrifying Problem
PuzzlOr problems are nice because they are simplified versions of real world problems of interest. Last December problem is a simplified version of an interesting logistics problem. A variety of method can be used to attack them, see for instance this interesting post by Isaac Slavitt where he tried both a brute force search and simulated annealing. Not surprisingly, I will try CPLEX on it. Here is the statement of the problem A new city is being built which will include 20 distinct neighborhoods as shown by the house... [More]
Tags: opl programming optimization modeling analytics |

## Decision makers need decision support
How can you make an optimization application be accepted by decision makers? The answer I gave that in my last post was to provide interactive applications. It so happens that colleagues of mine already discussed that in an IBM book Optimization and Decision Support Design Guide I can't resist quoting some of it given how it captures what I tried to express in my previous post. Decision makers need decision support Decision makers will not use any analytics tool unless they trust it. Trust arises... [More]
Tags: decision optimization analytics |

## Interactive Optimization
In my last post I discussed how gamification could be used to overcome the resistance to automated decision making systems. The case discussed in my previous post was about a system that computes retail prices for hotel rooms. The point of gaming was to show that human intervention would degrade the business outcome. Prices set by humans lead to less revenue than prices set by the system. Interesting comments on that post made me realize that I have been a bit extreme in my will to make a point. While I stand by my... [More]
Tags: optimization decision analytics |

## 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 |

## Optimization Is Ready For Big Data: Part 4, Veracity
Big Data promise is to enable better decisions based on data. The idea seems appealing yet there is a caveat: is the data reliable enough to base decisions on it? Question is to what extent can we trust data? My experience shows that cleaning data can take up to 80% of an analytics project. This is well known, and is often called the veracity dimension of Big Data . Point is that most data in the Big Data era is uncertain, see for instance the figure below, taken from a post by John Poppelaars... [More]
Tags: optimization uncertainty big_data analytics |

## Optimization Is Ready For Big Data: Part 3, Variety
A colleague of mine once told me that Big Data should be called "All Data". Indeed, one of the key dimension of Big Data is to apply analytics techniques to all kind of data. Other dimensions include volume and velocity of data. Can optimization be applied to all sorts of data? I'd say yes despite the fact that optimization primarily deals with numerical data. Indeed, optimization has already been applied to a wide variety of data, much more than common knowledge may suggest. Let's see a few... [More]
Tags: big_data analytics optimization |

## Optimization Is Ready For Big Data: Part 2, Velocity
Proponents of Big Data boast about how it might help get personalized behavior from all the things and systems people interact with (web sites, mobile apps, customer support services, internet of things, etc) . These systems have to deal with data in motion such as web interaction, sensor feeds (eg body temperature), video, social media feeds, etc. Dealing with such data is the velocity dimension of Big Data . I have discussed how optimization could be applied to another big data dimension, namely large volume of data, in my... [More]
Tags: analytics prescriptive big_data optimization |

## Optimization Is Ready For Big Data: Part 1, Volume
I had the honor to give a tutorial at a Big Data and Optimization seminar, thanks to an invitation from John Poppelaars , One of the topics I discussed seemed to resonate well. Let me try to explain it here. The first thing people think of when they hear about Big Data is large data volume. There are other dimensions than volume in Big Data, see Big Data For Dummies for instance, but let's focus on large data sets. Is current optimization technology ready for the huge data sets available now... [More]
Tags: predictive optimization prescriptive big_data analytics |

## Trying Decision Optimization on Cloud beta: part 2, CPLEX
In our previous entry we made a demo tour of our Decision Optimization on Cloud service. Let us now start real work with CPLEX. The first step is to get an IBM ID. If you don't have one yet, then you can start here . The next step is to go to the registration page that is accessible in various ways from the demo page . It lets you create an IBM ID if you don't have one. Assuming you have got one, we move to the next page by clicking on the Already have an IBM id? link.... [More]
Tags: analytics cloud saas optimization oaas |