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real2float is a set of libraries to analyze floating-point errors of polynomial programs with semidefinite programming. The tool requires Mono, Boogie2 and MATLAB toolboxes (Yalmip, SparsePOP and SeDuMi).


Mono Download

Boogie2 Download

MATLAB Download

Yalmip Download

SparsePOP Download

SeDuMi Download


Go the Source/ directory and execute the following command:

% xbuild Real2Float.sln


Go to the Examples/ directory and see the content of the rigidbody1.bpl program, encoded in Boogie language:

procedure rigidbody1 (x1 : real, x2 : real, x3 : real)

returns (f : real)

requires -15.0 <= x1 && x1 <= 15.0; requires -15.0 <= x2 && x2 <= 15.0; requires -15.0 <= x3 && x3 <= 15.0;

{ f := -x1 * x2 - 2.0 * x2 * x3 - x1 - x3; }

This program returns the result -x1 * x2 - 2.0 * x2 * x3 - x1 - x3, assuming that each variable lies in [-15, 15]. However, when taking into account floating-point error (e.g. with double precision), the actual computed result is different.

To perform this error analysis, one first executes the following command:

$ ../Source/bin/Debug/Real2Float.exe /bits:53 /resultPrecision:1e-12 rigidbody1.bpl >> ../Matlab/rigidbody1out.m

This generates a second Boogie program output.bpl, whose purpose is to verify whether the absolute error (on the result computation) is less than 1e-12 for double precision (64 bits with 53 bits of significand precision).

The file output.bpl displays the floating-point result f_float associated to the exact result:

f_float := (((-x1 * x2 * (1e0 + eps0) - 2 * x2 * (1e0 + eps1) * x3 * (1e0 + eps3)) * (1e0 + eps4) - x1) * (1e0 + eps2) - x3) * (1e0 + eps5);.

Note that error variables eps0,...,eps5 have been freshly generated for each operation *, +, - used to define f and is absolutely bounded by 2^(-53) (double precision).

In the Matlab/ directory, the script file rigidbody1out.m details how to analyze the error f - f_float using sparse sums of squares and semidefinite programming. Executing this script allows to generate certifates that the bound is less than 1e-12.

Further developments include automatic Matlab script generation and execution.


This is a preliminary implementation and the probability of finding bugs is high.

Do not hesitate to submit bug report of pull requests.

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