{"id":615,"date":"2021-09-02T11:08:00","date_gmt":"2021-09-02T11:08:00","guid":{"rendered":"https:\/\/elo-x.eu\/?p=615"},"modified":"2024-05-29T02:19:05","modified_gmt":"2024-05-29T02:19:05","slug":"lahcen-el-bourkhissi","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=615","title":{"rendered":"Lahcen El Bourkhissi"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tLahcen El Bourkhissi \n\t\t\t<\/h2 > \n\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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PhD Candidate in\u00a0<\/span>Engineering Science<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Polytechnic University of Bucharest<\/b><\/span><\/p><\/div>

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Lahcen El Bourkhissi, born in Morocco in 1997, earned his master’s degree in electrical engineering from the Arts et M\u00e9tiers Institute of Technology in Lille, France, where he completed a thesis on the sensorless control of multi-phase permanent magnet synchronous machines using neural networks. In October 2021, he joined the University POLITEHNICA of Bucharest to pursue a PhD under the supervision of Prof. Dr. Ion Necoara as a Marie-Curie fellow in the ELO-X project.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Lahcen primarily focuses on developing penalty\/augmented Lagrangian-based algorithms for solving nonlinear programs, which often arise from transcribing optimal control problems using direct methods. His main objective is to ensure that the developed methods involve only simple routines, making nonlinear model predictive control feasible for deployment on embedded hardware.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRead more about this project<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Publications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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1.<\/div>

Bourkhissi, Lahcen El; Necoara, Ion<\/p>

Convergence rates for an inexact linearized ADMM for nonsmooth optimization with nonlinear equality constraints Working paper<\/span> <\/p>

2024<\/span>, (Under review)<\/span>.<\/p>

BibTeX<\/a><\/span><\/p>

@workingpaper{bourkhissi2024convergence,
\r\ntitle = {Convergence rates for an inexact linearized ADMM for nonsmooth optimization with nonlinear equality constraints},
\r\nauthor = {Lahcen El Bourkhissi and Ion Necoara},
\r\nyear  = {2024},
\r\ndate = {2024-05-29},
\r\nurldate = {2024-05-29},
\r\nnote = {Under review},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {workingpaper}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div><\/div><\/div>
2.<\/div>
 Bourkhissi, Lahcen El;  Necoara, Ion<\/p>
Complexity of linearized quadratic penalty for optimization with nonlinear equality constraints<\/a> Working paper<\/span> <\/p>
2023<\/span>, (Under review)<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@workingpaper{bourkhissi2023complexity,
\r\ntitle = {Complexity of linearized quadratic penalty for optimization with nonlinear equality constraints},
\r\nauthor = {Lahcen El Bourkhissi and Ion Necoara},
\r\ndoi = {https:\/\/doi.org\/10.48550\/arXiv.2402.15639},
\r\nyear  = {2023},
\r\ndate = {2023-12-31},
\r\nabstract = {In this paper we consider a nonconvex optimization problem with nonlinear equality constraints. We assume that both, the objective function and the functional constraints, are locally smooth. For solving this problem, we propose a linearized quadratic penalty method, i.e., we linearize the objective function and the functional constraints in the penalty formulation at the current iterate and add a quadratic regularization, thus yielding a subproblem that is easy to solve, and whose solution is the next iterate. Under a dynamic regularization parameter choice, we derive convergence guarantees for the iterates of our method to an \u03f5 first-order optimal solution in O(1\/\u03f53) outer iterations. Finally, we show that when the problem data satisfy Kurdyka-Lojasiewicz property, e.g., are semialgebraic, the whole sequence generated by our algorithm converges and we derive convergence rates. We validate the theory and the performance of the proposed algorithm by numerically comparing it with the existing methods from the literature.},
\r\nnote = {Under review},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {workingpaper}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
In this paper we consider a nonconvex optimization problem with nonlinear equality constraints. We assume that both, the objective function and the functional constraints, are locally smooth. For solving this problem, we propose a linearized quadratic penalty method, i.e., we linearize the objective function and the functional constraints in the penalty formulation at the current iterate and add a quadratic regularization, thus yielding a subproblem that is easy to solve, and whose solution is the next iterate. Under a dynamic regularization parameter choice, we derive convergence guarantees for the iterates of our method to an \u03f5 first-order optimal solution in O(1\/\u03f53) outer iterations. Finally, we show that when the problem data satisfy Kurdyka-Lojasiewicz property, e.g., are semialgebraic, the whole sequence generated by our algorithm converges and we derive convergence rates. We validate the theory and the performance of the proposed algorithm by numerically comparing it with the existing methods from the literature.<\/div>
Close<\/a><\/p><\/div>