Augmented Lagrangian Method for Linear Programming Using Smooth Approximation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F24%3A43898145" target="_blank" >RIV/44555601:13440/24:43898145 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/10.1007/978-3-031-50320-7_13" target="_blank" >https://dl.acm.org/doi/10.1007/978-3-031-50320-7_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-50320-7_13" target="_blank" >10.1007/978-3-031-50320-7_13</a>
Alternative languages
Result language
angličtina
Original language name
Augmented Lagrangian Method for Linear Programming Using Smooth Approximation
Original language description
The augmented Lagrangian method can be used for finding the least 2 - norm solution of a linear programming problem. This approach?s primary advantage is that it leads to the minimization of an unconstrained problem with a piecewise quadratic, convex, and differentiable objective function. However, this function lacks an ordinary Hessian, which precludes the use of a fast Newton method. In this paper, we apply the smoothing techniques and solve an unconstrained smooth reformulation of this problem using a fast Newton method. Computational results and comparisons are illustrated through multiple numerical examples to show the effectiveness of the proposed algorithm.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-031-50319-1
ISSN
0302-9743
e-ISSN
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Number of pages
8
Pages from-to
186-193
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Berlín
Event location
Praha
Event date
Sep 3, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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