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Theory of algorithms for unconstrained optimization
Abstract
A few months ago, while preparing a lecture to an audience that included engineers and numerical analysts, I asked myself the question: from the point of view of a user of nonlinear optimization routines, how interesting and practical is the body of theoretical analysis developed in this field? To make the question a bit more precise, I decided to select the best optimization methods known to date – those methods that deserve to be in a subroutine library – and for each method ask: what do we know about the behaviour of this method, as implemented in practice? To make my task more tractable, I decided to consider only algorithms for unconstrained optimization.
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Citations
Cited by
Sequential quadratic programming
Boggs, P. T., & Tolle, J. W. (1995) · Acta Numerica · doi:10.1017/s0962492900002518 · Cited by 1,858
Smoothing methods for convex inequalities and linear complementarity problems
Chen, C., & Mangasarian, O. L. (1995) · Mathematical Programming · doi:10.1007/bf01592244 · Cited by 277
Metadata
- Authors
- Nocedal, J.
- Venue
- Acta Numerica
- Publisher
- Cambridge University Press (CUP)
- Published
- 1992-01
- Volume
- 1
- Pages
- 199-242
- ISSN
- 0962-4929 (ISSN-L)
- 1474-0508
- OpenAlex
- W2069752241
- Cited by
- 333
- References
- 0
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nocedal, J. (1992). Theory of algorithms for unconstrained optimization. Acta Numerica. https://doi.org/10.1017/s0962492900002270
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
