How to Install and Uninstall liblip-dev Package on Kali Linux

Last updated: May 20,2024

1. Install "liblip-dev" package

This tutorial shows how to install liblip-dev on Kali Linux


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                liblip-dev
            
                    
    
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2. Uninstall "liblip-dev" package

This guide let you learn how to uninstall liblip-dev on Kali Linux:


    
        
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                sudo apt remove
            
                        
                liblip-dev
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the liblip-dev package on Kali Linux

Package: liblip-dev
Source: liblip
Version: 2.0.0-2
Installed-Size: 276
Maintainer: Debian QA Group
Architecture: amd64
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), libtnt-dev, liblip2 (= 2.0.0-2)
Size: 67164
SHA256: ddf2ff9cb5b979bb1225faac07e5ae5cd10fb4938eb0d92ea4c98e7bc2d3d296
SHA1: c4bc83e3e82f3e81fd0af2c601e10a799fd2a936
MD5sum: ff1d416cca5de1025a8709c9bb05cd79
Description: reliable interpolation of multivariate scattered data
Lip interpolates scattered multivariate data with a Lipschitz function.
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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:
Homepage: http://www.deakin.edu.au/~gleb/lip.html
Tag: devel::library, field::mathematics, role::devel-lib
Section: libdevel
Priority: optional
Filename: pool/main/libl/liblip/liblip-dev_2.0.0-2_amd64.deb