Python

In the last post, we used Gurobi’s hierarchical optimization features to compute the Pareto front for primary and secondary objectives in an assignment problem. This relied on Gurobi’s setObjectiveN method and its internal code for managing hierarchical problems. Some practitioners may need to do this without access to a commercial license. This post adapts the previous example to use HiGHS and its native Python interface, highspy. It’s also useful to see what the procedure is in order to understand it better....

One of the first technology choices to make when setting up an optimization stack is which modeling interface to use. Even if we restrict our choices to Python interfaces for MIP modeling, there are lots of options to consider. If you use a specific solver, you can opt for its native Python interface. Examples include libraries like gurobipy, Fusion, highspy, or PySCIPOpt. This approach provides access to important solver-specific features such as lazy constraints, heuristics, and various solver settings....

Note: This post was updated to work with Python 3 and the 2nd edition of “Integer Programming” by Laurence Wolsey. We’ve been studying Lagrangian Relaxation (LR) in the Advanced Topics in Combinatorial Optimization course I’m taking this term, and I had some difficulty finding a simple example covering its application. In case anyone else finds it useful, I’m posting a Python version for solving the Generalized Assignment Problem (GAP). This won’t discuss the theory of LR at all, just give example code using Gurobi....

Note: This post was updated to work with Python 3 and PySCIPOpt. The original version used Python 2 and python-zibopt. It has also been edited for clarity. As a followup to the last post, I created another SCIP example for finding Normal Magic Squares. This is similar to solving a Sudoku problem, except that here the number of binary variables depends on the square size. In the case of Sudoku, each cell has 9 binary variables – one for each potential value it might take....

Note: This post was updated to work with Python 3 and PySCIPOpt. The original version used Python 2 and python-zibopt. It has also been edited for clarity. Back in October of 2011, I started toying with a model for finding magic squares using SCIP. This is a fun modeling exercise and a challenging problem. First one constructs a square matrix of integer-valued variables. from pyscipopt import Model # [...snip...] m = Model() matrix = [] for i in range(size): row = [m....

In response to this post, Ben Bitdiddle inquires: I understand the concept of using a companion set to remove duplicates from a list while preserving the order of its elements. But what should I do if these elements are composed of smaller pieces? For instance, say I am generating combinations of numbers in which order is unimportant. How do I make a set recognize that [1,2,3] is the same as [3,2,1] in this case?...

This is based on a lightning talk I gave at the LA PyLadies October Hackathon. I’m actually not going to go into anything much resembling algorithmic complexity here. What I’d like to do is present a common performance anti-pattern that I see from novice programmers about once every year or so. If I can prevent one person from committing this error, this post will have achieved its goal. I’d also like to show how an intuitive understanding of time required by operations in relation to the size of data they operate on can be helpful....

I find I have to build simulations with increasing frequency in my work and life. Usually this indicates I’m faced with one of the following situations: The need for a quick estimate regarding the quantitative behavior of some situation. The desire to verify the result of a computation or assumption. A situation which is too complex or random to effectively model or understand. Anyone familiar at all with simulation will recognize the last item as the motivating force of the entire field....

Note: This post was updated to work with NetworkX and for clarity. It’s possible this will turn out like the day when Python 2.5 introduced [coroutines][coroutines]. At the time I was very excited. I spent several hours trying to convince my coworkers we should immediately abandon all our existing Java infrastructure and port it to finite state machines implemented using Python coroutines. After a day of hand waving over a proof of concept, we put that idea aside and went about our lives....

Note: This post was edited for clarity. For the final JAPH in this series, I implemented a simple transpiler that converts a small subset of Scheme programs to equivalent Python programs. It starts with a Scheme program that prints 'just another scheme hacker'. (define (output x) (if (null? x) "" (begin (display (car x)) (if (null? (cdr x)) (display "\n") (begin (display " ") (output (cdr x))))))) (output (list "just" "another" "scheme" "hacker")) The program then tokenizes that Scheme source, parses the token stream, and converts that into Python 3....