How to Install and Uninstall liblip2 Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: November 24,2024

1. Install "liblip2" package

In this section, we are going to explain the necessary steps to install liblip2 on Ubuntu 21.04 (Hirsute Hippo)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                liblip2
            
                    
    
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2. Uninstall "liblip2" package

Please follow the guidance below to uninstall liblip2 on Ubuntu 21.04 (Hirsute Hippo):


    
        
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                sudo apt remove
            
                        
                liblip2
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the liblip2 package on Ubuntu 21.04 (Hirsute Hippo)

Package: liblip2
Architecture: amd64
Version: 2.0.0-1.2build1
Priority: optional
Section: universe/libs
Source: liblip
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Juan Esteban Monsalve Tobon
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 442
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5)
Filename: pool/universe/libl/liblip/liblip2_2.0.0-1.2build1_amd64.deb
Size: 289400
MD5sum: 4e966435cb9e3e1140b59c0dde54268a
SHA1: 5d3f3bbc9ef26273e45dfcaf7f30dde4c6454ec1
SHA256: 92c2a3e49e6a0768990e99db9efcfa1b54e0dd90ab35fc3b93d1b2d889d75f57
SHA512: 31de0b60a455438afa21c23db90b5828568b1742060f113b93a728727fc8728c8243f69cd89a4b04932fb8e15f18bfe57fae168959d3eed11576c3fdffa7f827
Homepage: http://www.deakin.edu.au/~gleb/lip.html
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