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SIAM Journal on Optimization is a peer-reviewed journal published by SIAM, covering Advanced Optimization Algorithms Research, Optimization and Variational Analysis, Sparse and Compressive Sensing Techniques. Over its lifetime it has published 2,950 papers, which have received 198,567 citations.
| Publisher | SIAM |
|---|---|
| Reference style | Numbered (SIAM) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, SIAM Journal on Optimization 12 (2023) 45–58.
Formats any DOI in the closest standard style — SIAM Journal on Optimization has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Advanced Optimization Algorithms Research Optimization and Variational Analysis Sparse and Compressive Sensing Techniques Matrix Theory and Algorithms Stochastic Gradient Optimization Techniques |
| ISSN | 1052-6234 |
| Citation impact (2-yr) | 2.71 |
| h-index | 188 |
| i10-index | 2,005 |
| Top institutions publishing here | Hong Kong Polytechnic University |
| Journal website | epubs.siam.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract. The Nelder–Mead simplex algorithm, first published in 1965, is an enormously popular direct search method for multidimensional unconstrained minimization. Despite its widespread use, essentially no theoretical results have been proved explicitly for the Nelder–Mead algorithm. This paper presents convergence properties of the Nelder–Mead algorithm applied to strictly convex functions in dimensions 1 and 2.…
Abstract. This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem and arises in many important applications as in the task of recovering a large matrix from a…
We propose a new trust region approach for minimizing a nonlinear function subject to simple bounds. Unlike most existing methods, our proposed method does not require that a quadratic programming subproblem, with inequality constraints, be solved in each iteration. Instead, a solution to a trust region subproblem is defined by minimizing a quadratic function subject…
This paper proposes an alternate method for finding several Pareto optimal points for a general nonlinear multicriteria optimization problem. Such points collectively capture the trade-off among the various conflicting objectives. It is proved that this method is independent of the relative scales of the functions and is successful in producing an evenly distributed set of…
Abstract. We consider the problem of finding the unconstrained global minimum of a realvalued polynomial p(x):R n → R, as well as the global minimum of p(x), in a compact set K defined bypolynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear matrix inequality(LMI) problems. A…
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