# liblip 2.0.0-1.2 source package in Ubuntu

## Changelog

liblip (2.0.0-1.2) unstable; urgency=low * Non-maintainer upload. * Add autotools-dev for updated config.{sub,guess} (Closes: #534817) -- Chen Baozi <email address hidden> Thu, 05 Jun 2014 16:22:13 +0800

## Upload details

- Uploaded by:
- Juan Esteban Monsalve Tobon on 2014-06-29

- Uploaded to:
- Sid

- Original maintainer:
- Juan Esteban Monsalve Tobon

- Architectures:
- any

- Section:
- math

- Urgency:
- Low Urgency

## See full publishing history Publishing

Series | Published | Component | Section | |
---|---|---|---|---|

Focal | release | on 2019-10-18 | universe | math |

Eoan | release | on 2019-04-18 | universe | math |

Disco | release | on 2018-10-30 | universe | math |

Cosmic | release | on 2018-05-01 | universe | math |

Bionic | release | on 2017-10-24 | universe | math |

Artful | release | on 2017-04-20 | universe | math |

Xenial | release | on 2015-10-22 | universe | math |

## Downloads

File | Size | SHA-256 Checksum |
---|---|---|

liblip_2.0.0-1.2.dsc | 1.7 KiB | 8ea77e80c24585be8bb7bc3c9b949db0df069984bf95a7c29377ef057bcd8952 |

liblip_2.0.0.orig.tar.gz | 666.2 KiB | 04cd1b87057e3ad3358a0731772fe010a00822f963d0e55d2a5b876ff16c010e |

liblip_2.0.0-1.2.diff.gz | 4.8 KiB | ce029f1cd974ae3b682d802cf32728ec60da6091e4a33fa3ca1d22989ed5b5a1 |

### Available diffs

No changes file available.

## Binary packages built by this source

- liblip-dev: 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)|<M| |x-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.

- liblip2: No summary available for liblip2 in ubuntu zesty.
No description available for liblip2 in ubuntu zesty.