# Generation of “random” multilinear polynomials for testing non-negativity algorithm

Multilinear polynomial is a multivariate polynomial where the exponent is zero or one. My instructor suggests to test my non-negativity algorithm with "random multilinear functions" which I call at the best red herring unless specifying the meaning of "random". Suppose you need to test your algorithm working with multilinear functions.

On which kind of "random" polynomials should a non-negativity algorithm be tested against?

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Multilinear polynomials are in a 1-1 correspondence with linear maps into $\mathbb{R}$, specifically

$$\sum_{i_m \in\{0,1\}} a_{i_1 \cdots i_n} x^{i_1} \cdots x^{i_n}$$

is isomorphic to a linear map $\mathbb{R}^{2^n} \to \mathbb{R}$, which can be specified with $2^n$ numbers. So you could generate a random linear map by generating $2^n$ random numbers, and map that to a multilinear polynomial.

Edit Here's an example of how to reproducably generate random numbers in Matlab:

>> seed = 147835;
>> rng(seed);      % Set initial seed
>> x = rand(5, 1); % Generate random numbers
>> rng(seed);      % Reset the seed
>> y = rand(5, 1); % Generate new random numbers
>> disp([x, y])
0.1171    0.1171
0.6895    0.6895
0.8330    0.8330
0.4596    0.4596
0.8468    0.8468


Note that the same random numbers are generated each time.

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Are you familiar with random number generation from a fixed seed? – Chris Taylor Dec 11 '13 at 13:39
Not mathematically but in practise yes. – hhh Dec 11 '13 at 13:57
Then you use a standard random number generator (e.g. MT19937) with an initial seed. Include your code and the seed in the paper - then any reader can reproduce your results. – Chris Taylor Dec 11 '13 at 14:00
I found out that the random data does not work realistically so now trying to find correlated binary numbers, moved the question here because apparently more programming related or? There is a paper "A simple method for generating correlated binary variates", reading... – hhh Dec 13 '13 at 0:14