CUTEr and SifDec: A constrained and unconstrained testing environment, revisited

By N. Gould, Dominique Orban, Philippe L. Toint. TOMS, 2003

664 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/e54937869fdb8c8af86bd46177bfb14d1722ec26

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Citations in the corpus

On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming (2006, methodology): “Section 4 presents numerical results of our implementation, called IPOPT, for the CUTEr test set [18], including a comparison of the filter method with a penalty function approach, and a comparison with two state-of-the-art nonlinear optimization codes, KNITRO [3, 28] and LOQO [23].”
Recent advances in trust region algorithms (2015, background): “Numerical results for a set of constrained problems from the CUTEr collection [56] indicate that this approach is very efﬁcient.”
Automated configuration of algorithms for solving hard computational problems (2009, methodology): “The optimization objective in their work was the runtime and number of function evaluations required by interior point methods for solving a set of large unconstrained regular problems from the CUTEr collection (Gould et al., 2004).”
NLEVP: A Collection of Nonlinear Eigenvalue Problems (2013, methodology): “In optimization we mention just the collections in the widely used Cute and Cuter testing environments [Bongartz et al. 1995; Gould et al. 2003], though various other, sometimes more specialized, collections are available.”
Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results (2011, methodology): “We give the results obtained by applying both Algorithms to all of the unconstrained problems from the CUTEr collection [16]; for those whose dimensions may be adjusted, we chose small variants simply so as not to overload our (Matlab) computing environment, most particularly the CUTEr interface to ”
Finding Optimal Algorithmic Parameters Using Derivative-Free Optimization (2006, methodology): “The trust-region algorithm described in section 4.2 with the classical parameter values pC was run on the 163 unconstrained regular problems from the CUTEr collection, using the default dimensions.”
A coordinate gradient descent method for nonsmooth separable minimization (2008, methodology): “We also chose 10 functions with n = 1,000 from the unconstrained problems in the CUTEr set [ 22 ].”
Benchmarking optimization solvers for structural topology optimization (2015, neutral): “1995), CUTEr (Gould et al. 2003), MIPLIB (Koch et al.”
An interior algorithm for nonlinear optimization that combines line search and trust region steps (2006, methodology): “We use the CUTEr collection [3, 18] as of June 5, 2003, from which 968 problems have been retained; the remaining problems have been discarded because they require too much memory.”
CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization (2013, neutral): “…of Namur CUTEST: a Constrained and Unconstrained Testing Environment with safe threads Gould, N. I. M.; Orban, Dominique; Toint, Ph Published in: Computational Optimization and Applications DOI: 10.1007/s10589-014-9687-3 Publication date: 2015 Document Version Early version, also known as…”
Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization” (2011, methodology): “It is not surprising that the procedure described before improves the performance of the algorithm since it it known that it is productive to explore the boundary of the feasible region during the subspace minimization phase [Lin and Mor´ e 1999; Gould et al. 2003b].”
Smoothing and worst-case complexity for direct-search methods in nonsmooth optimization (2013, methodology): “We have made a number of experiments regarding the use of smoothing in direct search, on a test set suggested in [28] consisting of 53 problems of the form minx∈Rn f(x) = ‖F (x)‖1, where F varies among 22 nonlinear vector functions of the CUTEr collection [19] with 2 ≤ n ≤ 12 and different initial p”