Nonlinear rescaling
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Revision as of 09:32, 10 May 2022 by imported>Len Stevenson (fix)
Nonlinear rescaling is a method of transforming a function consisting of the constraints of an optimization problem into another equation/problem that is equivalent to the optimal solution for said problem. [1][2]
History
Roman Polyak published the original paper, titled Nonlinear rescaling and proximal-like methods in convex optimization in February of 1997.[1] The method has seen application in medical [3] and remote sensing settings.[4]
References
- ↑ 1.0 1.1 Polyak, Roman; Teboulle, Marc (1997). "Nonlinear rescaling and proximal-like methods in convex optimization". Mathematical Programming (Springer Science and Business Media LLC) 76 (2): 265–284. doi:10.1007/bf02614440. ISSN 0025-5610.
- ↑ Polyak, R.; Ho, S. S.; Griva, I. (2007). "Support vector machine via nonlinear rescaling method". Optimization Letters 1 (4): 367-378.
- ↑ Wei, Bo; Haskell, William B.; Zhao, Sixiang (2020-03-24). "A Randomized Nonlinear Rescaling Method in Large-Scale Constrained Convex Optimization". https://arxiv.org/abs/2003.10888v1.
- ↑ Afshar, M. H.; Yilmaz, M. T. (2017). "The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products". Remote Sensing of Environment 196: 224-237.
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