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Graph Coloring: Column Generation or Column Enumeration? - Spaghetti Optimization

http://stegua.github.io/blog/2015/07/04/column-enumeration

My cookbook about Math, Algorithms, and Programming. Graph Coloring: Column Generation or Column Enumeration? In this post, I like to share a simple idea on how to solve to optimality some hard instances. Of the Graph Coloring. Problem. This simple idea yields a new time record for a couple of hard instances. To date, the best exact. Approach to solve Graph Coloring is based on. A key component of Branch-and-Price is the. Can solve a few hard instances of graph coloring with the following. Formulation&#4...

stegua.github.io

From blackboard to code: Gomory Cuts using CPLEX - Spaghetti Optimization

http://stegua.github.io/blog/2013/02/05/gomory-cuts-with-cplex

My cookbook about Math, Algorithms, and Programming. From Blackboard to Code: Gomory Cuts Using CPLEX. Edited on May 16th, 2013. Fixes due to M. Chiarandini. On the blackboard, to solve small Integer Linear Programs with 2 variables and. Constraints is easy, since they can be plotted in the plane and the linear relaxation can be solved geometrically. You can draw the lattice of integer points, and once you have found a new cutting plane, you show that it. The optimum solution of the LP relaxation. The op...

stegua.github.io

Blog Archive - Spaghetti Optimization

http://stegua.github.io/blog/archives

My cookbook about Math, Algorithms, and Programming. Exercise in Python: remove blanks from strings. Graph Coloring: Column Generation or Column Enumeration? Big Data and Convex Optimization. Posted in Big Data. The Impact of Preprocessing on the MIPLIB2003. An Informal Report from the Combinatorial Optimization Workshop @ Aussois 2014. Posted in Combinatorial Optimization. Public Transport and Big Data. The Researcher’s Bible. GeCol: a Graph Coloring solver on top of Gecode. Posted in DIMACS Challenge.

stegua.github.io

An Informal Report from the Combinatorial Optimization Workshop @ Aussois 2014 - Spaghetti Optimization

http://stegua.github.io/blog/2014/01/13/informal-report-from-cow-at-aussois-2014

My cookbook about Math, Algorithms, and Programming. An Informal Report From the Combinatorial Optimization Workshop @ Aussois 2014. It is very hard to report about the Combinatorial Optimization Workshop. It was like an informal IPCO. In the audience, leaded by. Who appears at work in the following photo-tweet:. Egon talks intersection cuts at #aussois. Still the man. pic.twitter.com/7KMcNyJYV0. Mdash; Jeff Linderoth (@JeffLinderoth) January 8, 2014. A new paper enters my PAPERS-TO-BE-READ folder. The i...

stegua.github.io

How Italian Commuters Discovered Operations Research - Spaghetti Optimization

http://stegua.github.io/blog/2012/12/16/how-italian-commuters-discovered-or

My cookbook about Math, Algorithms, and Programming. How Italian Commuters Discovered Operations Research. Last week, more then 700,000 Italian commuters discovered the importance of Operations Research (OR). Nobody explicitly mentioned OR, but due to a horrible crew schedule of Trenord. A train operator with around 330 trains and 2700 employees), the commuters had a long, long, long nightmare. If you want to get an idea of the chaos you can search for caos tilt software trenord on google.it. To improve ...

stegua.github.io

Backtrack Programming in c - Spaghetti Optimization

http://stegua.github.io/blog/2013/03/22/backtrack-programming-in-c

My cookbook about Math, Algorithms, and Programming. Backtrack Programming in C. Recently, I have discovered a nice tiny library (1 file! That supports Backtrack Programming. The library is called CBack. And is developed by Keld Helsgaun. Who is known in the Operations Research and Computer Science communities for his efficient implementation of the Lin-Kernighan heuristics for the Travelling Salesman Problem. Offers basically two functions that are described in [1] as follows:. Problem and the 15-puzzle.

stegua.github.io

Public Transport and Big Data - Spaghetti Optimization

http://stegua.github.io/blog/2013/11/17/public-transport-and-big-data

My cookbook about Math, Algorithms, and Programming. Public Transport and Big Data. A simple query for Big Data on Google gives about 26,700,000 results. Is not really a. But still on Google you can get almost the same number as with Big Data : 26,400,000 results. Why is Public Transport so important? Of us use Public Transport. Every day, but. Well, for time, it is not always true, but it happens more often than commonly perceived). Thus, an important challenge. And use Public Transport. However, new pr...

stegua.github.io

The Impact of Preprocessing on the MIPLIB2003 - Spaghetti Optimization

http://stegua.github.io/blog/2014/03/19/the-impact-of-preprocessing

My cookbook about Math, Algorithms, and Programming. The Impact of Preprocessing on the MIPLIB2003. What do you know about preprocessing. Mixed Integer Programming (MIP). After a nice chat with Bo Jensen. CEO, founder, and co-owner (really, he is a Rocket Scientist! I realised that I know barely anything. By definition, we have that:. All I know is that every MIP solver has a Presolve. Parameter, which can take different values. For instance, Gurobi. No presolve at all. A method to measure. As a solver, ...

stegua.github.io

Credits - Spaghetti Optimization

http://stegua.github.io/credits

My cookbook about Math, Algorithms, and Programming. This blog was started after several discussions with my friend and coauthor Stefano Coniglio. Who always proofreads my posts. Though I have not convinced him to write a guest post yet, he is a contributor of this blog: thanks Stefano! Another friend and coauthor that supports this blog is Marco Chiarandini. I do not know how he finds the time to proofread my posts, but he does, with the same seriousness as if he were reviewing a paper: thanks Marco!

stegua.github.io

GeCol: a Graph Coloring solver on top of Gecode - Spaghetti Optimization

http://stegua.github.io/blog/2013/06/28/gecol

My cookbook about Math, Algorithms, and Programming. GeCol: A Graph Coloring Solver on Top of Gecode. This post is about solving the classical Graph Coloring. Problem by using a simple solver, named here GeCol. That is built on top of the Constraint Programming (CP) solver Gecode. The approach of GeCol is based on the CP model described in [1]. Here, we want to explore some of the new features of the last version of Gecode (version 4.0.0), namely:. Lightweight Dynamic Symmetry Breaking (LDSB). It is poss...