LAUSR

A note: minimizing total absolute deviation of job completion times on unrelated machines with general position-dependent processing times and job-rejection

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0 Published in 2018 at "Annals of Operations Research"

DOI: 10.1007/s10479-018-2779-1

Abstract: We study a scheduling problem with the objective of minimizing total absolute deviation of completion times (TADC). TADC is considered here in the most general form studied so far: the machine setting is that of… read more here.

Keywords: minimizing total; absolute deviation; processing; completion times ... See more keywords

Optimization techniques for multivariate least trimmed absolute deviation estimation

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0 Published in 2017 at "Journal of Combinatorial Optimization"

DOI: 10.1007/s10878-017-0109-1

Abstract: Given a dataset an outlier can be defined as an observation that does not follow the statistical properties of the majority of the data. Computation of the location estimate is of fundamental importance in data… read more here.

Keywords: absolute deviation; trimmed absolute; least trimmed; optimization ... See more keywords

One-layer neural network for solving least absolute deviation problem with box and equality constraints

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0 Published in 2019 at "Neurocomputing"

DOI: 10.1016/j.neucom.2018.11.037

Abstract: Abstract This paper presents a neural network for solving least absolute deviation problems with equality and box constraints. Compared with some existing models, the proposed neural network has fewer state variables and only one-layer structure.… read more here.

Keywords: absolute deviation; network; solving least; network solving ... See more keywords

Regularized least absolute deviation-based sparse identification of dynamical systems.

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2 Published in 2023 at "Chaos"

DOI: 10.1063/5.0130526

Abstract: This work develops a regularized least absolute deviation-based sparse identification of dynamics (RLAD-SID) method to address outlier problems in the classical metric-based loss function and the sparsity constraint framework. Our method uses absolute derivation loss… read more here.

Keywords: least absolute; identification; absolute deviation; based sparse ... See more keywords

A Joint Least Squares and Least Absolute Deviation Model

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1 Published in 2019 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2019.2897863

Abstract: We propose a joint least squares and least absolute deviations (JOLESALAD) model, show that the proposed model can cover least absolute shrinkage and selection operator (LASSO) and two of its variants, namely the generalized LASSO (gLASSO) and… read more here.

Keywords: squares least; least squares; joint least; model ... See more keywords