How to Install and Uninstall liblip2 Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: May 10,2024

1. Install "liblip2" package

Please follow the step by step instructions below to install liblip2 on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
            $
        

        
            
                sudo apt update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt install
            
                        
                liblip2
            
                    
    
    Copied

2. Uninstall "liblip2" package

Please follow the guidelines below to uninstall liblip2 on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
            $
        

        
            
                sudo apt remove
            
                        
                liblip2
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the liblip2 package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: liblip2
Priority: optional
Section: universe/libs
Installed-Size: 456
Maintainer: Ubuntu Developers
Original-Maintainer: Juan Esteban Monsalve Tobon
Architecture: amd64
Source: liblip
Version: 2.0.0-1.2
Depends: libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1)
Filename: pool/universe/libl/liblip/liblip2_2.0.0-1.2_amd64.deb
Size: 289596
MD5sum: 67a2bf2829961c1ad64351264f7e342c
SHA1: 2bd1afe0a41f37f5402665fb79fa8fdf3fdbc511
SHA256: 6cd73fbf73ec5e393cbfa45880251eb2f89b850af61cae34dbc94da2ee442d28
Description-en: reliable interpolation of multivariate scattered data
Lip interpolates scattered multivariate data with a Lipschitz function.
.
Methods of interpolation of multivariate scattered data are scarce.
The programming library Lip implements a
new method by G. Beliakov, which relies on building reliable lower and
upper approximations of Lipschitz functions. If we assume that the
function that we want to interpolate is Lipschitz-continuous, we can
provide tight bounds on its values at any point, in the worse case
scenario. Thus we obtain the interpolant, which approximates the unknown
Lipschitz function f best in the worst case scenario. This translates
into reliable learning of f, something that other methods cannot do (the
error of approximation of most other methods can be infinitely large,
depending on what f generated the data).
.
Lipschitz condition implies that the rate of change of the function is
bounded:
.
|f(x)-f(y)| .
It is easily interpreted as the largest slope of the function f. f needs
not be differentiable.
.
The interpolant based on the Lipschitz properties of the function is
piecewise linear, it possesses many useful properties, and it is shown
that it is the best possible approximation to f in the worst case
scenario. The value of the interpolant depends on the data points in the
immediate neigbourhood of the point in question, and in this sense, the
method is similar to the natural neighbour interpolation.
.
There are two methods of construction and evaluation of the interpolant.
The explicit method processes all data points to find the neighbours of
the point in question. It does not require any preprocessing, but the
evaluation of the interpolant has linear complexity O(K) in terms of the
number of data.
.
"Fast" method requires substantial preprocessing in the case of more
than 3-4 variables, but then it provides O(log K) evaluation time, and
thus is suitable for very large data sets (K of order of 500000) and
modest dimension (n=1-4). For larger dimension, explicit method becomes
practically more efficient. The class library Lip implements both fast
and explicit methods.
Description-md5: 4ee83e31f3d395f9f7925d0040a719d4
Homepage: http://www.deakin.edu.au/~gleb/lip.html
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu