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# Welcome to the MOCHA page

This page is dedicated to the COIN-OR project MOCHA which stands for *Matroid Optimization: Combinatorial Heuristics and Algorithms*. MOCHA provides heuristics and algorithms to solve problems in the burgeoning field of multicriteria matroid optimization. Besides specific algorithms and heuristics, included are data structures to store and work on matroids, in order to provide a foundation for old and new algorithms. MOCHA was initially developed for the paper "Computation in Multicriteria Matroid Optimization", submitted to the Journal of Experimental Algorithmics, 2009, and authored by J. De Loera, D. Haws, J. Lee, and A. O'Hair.

# Project Manager

Dr. David Haws

dchaws+MOCHA@…

University of Kentucky

Department of Statistics

871 Patterson Office Tower

Lexington, KY 40506-0027

# Installation

svn co https://projects.coin-or.org/svn/MOCHA/stable/0.9 MOCHA

cd MOCHA

./configure

make

make install

## Third-party software dependencies

GMP, LAPACK, BLAS. MOCHA can compile without GMP, though some functionality will be lost such as rank calculation using arbitrary precision arithmetic.

More information is available in the INSTALL file.

# Bug Report

https://projects.coin-or.org/MOCHA/newticket

# FAQ

# Matroid Optimization

Matroids encapsulate the combinatorial notion of independence. They can be found in graphs, matrices, point sets, hyperplane arrangements and many more. There are many ways to define a matroid, but for MOCHA the most useful axiomatization is that of bases. Given a finite set S (ground set) with n elements, the collection of subsets (B) of S are the bases for a matroid M if

1) every element of (B) has the same cardinality, 2) if B1 and B2 are in (B), for all x in B1 not in B2, there exists y in B2 not in B1 such that B1 - x + y is in (B).

Perhaps the simplest matroid example is given by a graph. Given a graph G, the bases of the matroid on G are all the spanning trees of G. It is not hard to see that the collection of spanning trees of G all have the same cardinality and the exchange property 2) holds.

Another intuitive example is given by a matrix A. The ground set is the labeled columns of A, and a base of the matroid on A is any maximally linear independent collection of columns of A. ( Mat[roid/rix] )

For more on matroids see: "Matroid Theory", Welsh, 1976, "Matroid Theory", Oxley, 1992 or for a sufficient online coverage http://en.wikipedia.org/wiki/Matroid.

Matroid optimization appears when weights are placed on the ground set S. For example, by placing a single real valued weight on every element of S, every base B1 has a weight given by the sum of the weights it contains. One could then optimize over the bases of a matroid by asking for the base with maximal (minimal) weight. This problem can be solved by the strongly polynomial greedy algorithm (which MOCHA has a function for).

MOCHA can tackle an even larger family of optimization problems. Instead of
placing a single real value weight on each element of S, one places d real
valued weights on every element of S. In this case, instead of a base B1 having
a real valued weight, the weight of B1 is a vector in R^{d}, given by the sum of
the weights it contains. To ask for an optimal base, given multiple weightings,
one needs a way to decide between two vectors in R^{d}. There are multiple
methods to distinguish two points in R^{d}, such as a functional f: R^{d} --> R,
Pareto inequality, or min-max. We call these balancing functions, and they may
be linear, convex, or highly non-linear.

The difficulty of matroid optimization (with multiple weightings) lays in the
fact that there are exponentially many bases with respect to n, the size of the
ground set. So, one can not simply iterate over all bases of a matroid and
optimize. Fortunately, under some assumptions on the weightings applied to the
ground set S, the number of points obtained by considering the weights of all
bases is polynomially bounded. There are deterministic algorithms to compute
the projected bases (projected by the weighting on S), but they are
computationally *heavy*. See "Nonlinear Matroid Optimization and Experimental
Design", Berstein et. al., 2008.

MOCHA is a software packaged developed in conjunction with the paper "Computation in Multicriteria Matroid Optimization", submitted to the ACM Journal of Experimental Algorithmics, 2009. Its goals are to provide fast heuristics and algorithms aimed at solving multiple weight matroid optimization problems. Typically, the choice of weightings on the ground set ensure that the number of projected bases is much smaller than all bases. Many of MOCHA's heuristics attempt to give all the projected bases (the most difficult part), leaving the final optimization over a balancing function for later.

The software in MOCHA will take in as input a matroid (currently supported formats are matrices, graphs, uniform) and the weightings on the ground set. It will output a subset of the projected bases (guaranteed to be all projected bases in some cases).

# How MOCHA works

Data Format:

The input format for MOCHA is a text file in the following format:

<MATROID TYPE>

<MATROID DESCRIPTION>

...

<Number of weightings/criteria>

<Number of matroid elements>

<#> <#> ... <#>

<#> <#> ... <#>

...

<#> <#> ... <#>

Example (Instances/Calibration?/gn9e18d2w0w20.mo)

GRAPH

ADJACENCY

9

9

1 0 0 0 0 1 0 1 0

0 1 0 0 1 1 1 1 0

0 0 1 0 0 0 0 1 0

0 0 0 1 1 0 0 1 0

0 1 0 1 1 1 1 1 0

1 1 0 0 1 1 1 1 1

0 1 0 0 1 1 1 1 1

1 1 1 1 1 1 1 1 1

0 0 0 0 0 1 1 1 1

2

18

18 1 7 1 8 7 14 17 6 9 20 6 10 3 8 5 4 16

1 18 0 4 16 17 19 1 18 5 1 11 4 17 10 9 19 16

MATROID TYPE can be either "GRAPH" or "VECTOR". If the type is "GRAPH" then the next line should be "ADJACENCY". Following this the adjacency representation of the graph should follow. It should be in the format:

<Number of rows>

<Number of columns>

followed by an appropriate row and column of numbers.

If the type is "VECTOR" then the following lines should be a matrix given as

<Number of rows>

<Number of columns>

followed by an appropriate row and column of numbers.

After the matroid type is specified, the weight is given as a matroid given as

<Number of rows>

<Number of columns>

followed by an appropriate row and column of numbers.

An example of a vector matroid representation is:

VECTOR

3

10

0 6 8 7 0 10 6 10 9 10

3 2 0 6 10 3 5 1 5 5

4 5 4 9 1 0 1 7 10 6

2

10

1 7 1 5 7 8 3 7 5 2

3 8 7 7 0 6 5 3 8 5

What MOCHA does: The most important program is 'matroidtest' which contains heuristics/algorithms which output subsets of the projected bases. The projected bases are outputted in matlab format. The file should also be easy to parse using GNU utilities. We hope to add more output formats.

The three general methods to find subsets of the projected bases are:

DFBFS: Different Fiber BFS.

This is a modified version of breadth first search which only pivots to new basis
as long as it is a new projected basis.

Pivot Test:

This finds a tight rectangular box containing all projected basis then proceeds
to use local search or tabu search on each point (many times if specified), using
a specialized convex function. All the projected bases found are output.

Boundary:

This outputs the vertices boundary of the convex hull of the projected basis.
Our heuristic also has the side effect of outputting some extreme points besides
the vertices.

For further description of our algorithms heuristics, see below.

# Instances from our Paper

The directory 'Instances/' contains all of our input data for the paper as well as some simple examples demonstrating our software.

Detailed How-To/Example:

To reproduce most of the tests in our paper, only three programs are needed 'matroidtest', 'localsearch', and 'tabusearch'. Below we give instructions how to reproduce experiments shown in our paper.

The program 'matroidtest' has the capability of running the following algorithms: Different Fiber BFS, Pivot Test, Projected Boundary Calculation, Pareto Calculation, and Brute force projected bases calculation (Graphs only). See our paper for a full description of these algorithms. 'matroidtest' has the option to output all computed points in a MATLAB ready file which will plot the points.

The program 'matroidtest' can be run with no arguments. It is interactive and will ask which algorithms to run. If more than one are selected, e.g. DFBFS and Boundary calculation, then all the points will be output to the same file, with different labels.

'matroidtest' can also be invoked as:

matroidtest <inputfile>

or

matroidtest <inputfile> <outputfile>

Different Fiber BFS:

Run 'matroidtest'. Specify the input file either in the command line or when prompted. Next it will ask "Different Fiber BFS Enumerations? (y/n)". Enter "y". It will ask for the number of searches. This is the number of DFBFS searches to perform on the interior and boundary. 'matroidtest' will perform this number of searches using (pseudo)random projected boundary points. Next, 'matroidtest' starts at a random unvisited projected base a number of times specified above. Next 'matroidtest' will ask for the BFS depth. This is the recursion level that DFBFS will terminate at. Following this, 'matroidtest' will ask for the "Interior Random retry limit?". This is the limit of how many times DFBFS will try to find a new random projected bases before giving up. 'matroidtest' will then ask for the "Boundary random retry limit?". This is the number of times DFBFS will try to find a new random projected base on the boundary before giving up. When prompted for all other algorithms/test, enter "n". When prompted for the output file, give a name. Alternatively enter the output filename as the second command-line argument.

Here is a quick example:

Run the following command from the src/ directory:

./matroidtest ../Instances/Calibration?/gn11e20d2w0w100.mo tmpout.m

Enter the following sequence of input:

y

100

8

10000

100

n

n

n

n

n

n

n

This will run DFBFS 100 times, with a truncation level of 8, an interior retry limit of 10000, and a boundary retry limit of 100. Typically it takes MOCHA longer to find a random projected boundary point than an interior point. It will write the outputed points to tmpout.m. In MATLAB execute 'tmpout.m'.

Local Search/Tabu? Search:

The objective for local/tabu search must be hard coded into the files localsearch.cpp and tabusearch.cpp and recompiled. By default the object function is set to x

^{2. We did not want to spend excessive time implementing or including a bulky format to read in arbitrary objective functions. To change the objective function, study the function "MyFct?" in localsearch.cpp or tabusearch.cpp. }

localsearch and tabusearch are fully interactive and will ask for all the necessary arguments. Alternatively they can be invoked as

localsearch <inputfile> <outputfile> <number of searches>

tabusearch <inputfile> <outputfile> <number of searches> <tabu searchlimit>

Again, the output files are written in MATLAB format which can be run to display graphically the local and tabu searches.

Example from within /src/ directory:

./localsearch ../Instances/Calibration?/gn11e20d2w0w20.mo tmp.m 100

This will use gn11e20d2w0w20.mo as input, output to the file tmp.m and run 100 local searches.

Auto Box Pivot Heuristic:

This will perform the Pivot Test described in our paper. Run 'matroidtest', specify input and output files and when asked "Run Auto Box Pivot Heuristic (Local Search)? (y/n)" or "Run Auto Box Pivot Heuristic (Tabu Search)? (y/n)" select y according to which pivot heuristic you want to use. 'matroidtest' will then ask for the number of times to run local search or tabu search on each point. For tabu search, 'matroidtest' will also ask for the tabu search limit. The auto box pivot heuristic will then automatically calculate an appropriate bounding box of the projected bases using 2(number of weightings) local searches.

An example run (for localsearch) would be as follows:

Run the command from the src/ directory:

./matroidtest ../Instances/Calibration?/gn11e20d2w0w100.mo tmpout.m

Enter the following sequence of input:

n

y

20

n

n

n

n

n

n

This will run the auto box pivot heuristic using local search using 20 attempts for each point.

Box Pivot Heuristic:

This is much the same as the Autobox heuristic above in 'matroidtest' except that the user is prompted for coordinates of a box of points to test using Pivot Test.

Boundary Calculation:

When there are only two criteria/weightings 'matroidtest' can calculate the boundary (and more) of the projected bases. When asked "Run Boundary Calculation (dim=2 only)? (y/n)" enter "y".

Pareto Optimum Test:

There are four options for finding Pareto optima:

1) Boundary Only,

2) Boundary Triangular Region Search,

3) BFS Pareto Search, and

4) BFS and Boundary Triangular Region Pareto Search.

Boundary Only: This will compute the boundary of the projected bases.

'matroidtest' will then output the Pareto optima that are contained in the boundary.

Boundary Triangular Region Search: This follows the Boundary and

Triangular Region Pareto Test heuristic described in our paper. 'matroidtest' will compute the boundary, find regions where the remaining Pareto optima will be, then run the Pivot Heuristic using either local search or tabu search to enumerate a subset of potential Pareto optima.

BFS Pareto Search: This will perform Different Fiber BFS and from the

returned set, compute the Pareto optima. Note that these are only guaranteed to be Pareto optima of the points found by DFBFS, which may not necessarily be Pareto optima of the original problem.

BFS and Boundary Triangular Region Pareto Search: This will perform

DFBFS and Boundary Triangular Region Search and combine the results.

Brute Force Enumeration:

'matroidtest' can also enumerate all projected bases using reverse search when the matroid is graphical. This uses the algorithm of Matsui.

# Executables contained in the MOCHA package

alltoproj

This file is meant to convert the output of findChildren printing all spanning trees and project them by some given weighting. Expects input to be sets given by unsigned ints on each line. First argument should be the matrix that is our weighting. The second argument should be the number of elements in each set

designtomatrix

This program will read in two matrices; a n x k exponent vector matrix B, a m x k design point matrix P

It then computes A_ij := \prod_{h=1}

^{k P_{i,h}}B_{j,h} It reads in the matrices from standard input and prints the matrix to standard output

estimatebases

This program estimates the number of bases of a matrix by assigning random weights to each column of the matrix and finding the maximal weight basis. This is done m times and the resulting average estimates the function GAMMA. Then upper and lower bounds for the number of bases are calculated.

genmatrix

Generates a random matrix. Useful for creating random examples. run genmatrix for input format.

genrandmo

This is an interactive program used to generate random Vector and Graph matroids along with random weightings.

graphtest

Program to check validity of some internal graph routines.

localsearch

Program to perform localsearch heuristic as described in our paper. See explanation above for usage.

matroidtest

The program 'matroidtest' has the capability of running the following algorithms: Different Fiber BFS, Pivot Test, Projected Boundary Calculation, Pareto Calculation, and Brute force projected bases calculation (Graphs only). See our paper for a full description of these algorithms.

mvbalclust

Non-linear matroid optimization can solve the problem of min variance balanced clustering. The input is a file that is a matrix of 2k points in R

^{n which are to be partitioned into two sets of size k such that the variance of their euclidean distances is minimized. Input is a file with the following format. <Number of rows> <Number of columns> followed by an appropriate row and column of numbers. }

nagibatest

This program was written to test the our implementation of Matsui and Nagamochi-Ibariki algorithms. The input is ADJACENCY <Number of rows> <Number of columns> followed by an appropriate row and column of numbers. The previous matrix is the adjacency representation of the graph.

nagibatest will enumerate all spanning trees and only print out the total number of trees. The code can be modified easily to print out all trees to stdout. Set "printTrees = 1;".

point2po

This program reads in from stdin points, and finds the pareto optimum using a straightforward search

tabusearch

Program to perform localsearch heuristic as described in our paper. See explanation above for usage.

testmatrix

Program to internally check matrix class.

Data structures and code:

Effort was made to make the code legible and intuitive. The authors hope to continue to add functionality and subroutines to many of the objects. Others are welcome and encouraged to contribute!