1993
DOI: 10.1016/0020-7683(93)90220-2
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An iterative algorithm for limit analysis with nonlinear yield functions

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Cited by 94 publications
(56 citation statements)
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“…Recently, the interest of scientists in numerical limit analysis [9][10][11][12][13][14][15] has been resurged, principally thanks to the rapid development of efficient optimization algorithms and the continuous improvement in computer facilities. Current research is focusing on developing numerical limit analysis tools which are efficient and robust for the practice usage of engineers.…”
mentioning
confidence: 99%
See 1 more Smart Citation
“…Recently, the interest of scientists in numerical limit analysis [9][10][11][12][13][14][15] has been resurged, principally thanks to the rapid development of efficient optimization algorithms and the continuous improvement in computer facilities. Current research is focusing on developing numerical limit analysis tools which are efficient and robust for the practice usage of engineers.…”
mentioning
confidence: 99%
“…For the NLP algorithms, some recently important contributions can be mentioned such as the algorithm based on feasible directions by Zouain et al [10] or by Lyamin and Sloan [19], the algorithm based on the interior-point method by Andersen et al [20] or by Krabbenhoft and Damkilde [21], and the general-purpose NLP codes CONOPT and MINOS by Tin-Loi and Ngo [22]. Recently, one of the most efficient NLP algorithms based on the primal-dual interior-point method was proposed by Andersen et al [23].…”
mentioning
confidence: 99%
“…While linear programming involves piecewise linear yield functions, nonlinear programming involves nonlinear yield surfaces. Much progress has been made in developing numerical procedures for limit analysis problems [5][6][7][8][9][10]. Current research is focussing on developing limit analysis tools which are sufficiently efficient and robust to be of use to engineers working in practice.…”
Section: Introductionmentioning
confidence: 99%
“…Various techniques have been proposed in the literature to overcome this singularity problem. These include linearization of the yield condition [32], regularization of the plastic dissipation function [33][34][35][36], and a direct iterative algorithm [37,38]. One of the most robust and efficient algorithms to overcome this difficulty is the primal-dual interior-point method presented in [39,40] and implemented in commercial codes such as the Mosek software package.…”
Section: Introductionmentioning
confidence: 99%