How to Install and Uninstall liblip2 Package on Kali Linux

Last updated: May 18,2024

1. Install "liblip2" package

Here is a brief guide to show you how to install liblip2 on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                liblip2
            
                    
    
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2. Uninstall "liblip2" package

Here is a brief guide to show you how to uninstall liblip2 on Kali Linux:


    
        
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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 Kali Linux

Package: liblip2
Source: liblip
Version: 2.0.0-2
Installed-Size: 443
Maintainer: Debian QA Group
Architecture: amd64
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1)
Size: 292724
SHA256: 65b1d702ab8b8139143fbdb8939feca6e1a41633cbde236a8284d491093e9c0b
SHA1: 0fd768607a602727415fa86131a22da10ef8f24a
MD5sum: 0d7ffcfbf5f6be95ad000b147b288dc7
Description: 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.
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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:
Homepage: http://www.deakin.edu.au/~gleb/lip.html
Tag: role::shared-lib
Section: libs
Priority: optional
Filename: pool/main/libl/liblip/liblip2_2.0.0-2_amd64.deb