[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-950868","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n  \u003Cdiv class=\u0022row\u0022\u003E\n    \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n      \u003Ch2 class=\u0022h1 text-left\u0022\u003E  [M1 DiPaQ] Optimisation pour le calcul scientifique\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          3\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemestre calendaire :\n          Semestre 2\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                    \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EAnglais\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003Enon\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n    \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EPr\u00e9requis\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003EPrerequisites:\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003ELinear algebra\u003C\/li\u003E\n\u003Cli\u003EMultivariable calculus\u003C\/li\u003E\n\u003Cli\u003EBasics in quantum computing\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003ECourse program, plan, content:\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003ETotal duration:\u003C\/strong\u003E\u0026nbsp;21 hours (7 sessions \u00d7 3 hours)\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EIntroductions and background (convexity, differentiability, optimality conditions, convergence rates \u2026)\u003C\/li\u003E\n\u003Cli\u003EContinuous\u0026nbsp;optimization\u0026nbsp;(first order methods: Gradient methods, line search, Acceleration)\u003C\/li\u003E\n\u003Cli\u003EContinuous\u0026nbsp;optimization\u0026nbsp;(second order methods: Newton methods including Quasi-Newton, secant, IRLS)\u003C\/li\u003E\n\u003Cli\u003EConjugate Gradient Methods\u003C\/li\u003E\n\u003Cli\u003ELinear conjugate gradient\u003C\/li\u003E\n\u003Cli\u003ESolution of linear systems\u003C\/li\u003E\n\u003Cli\u003ELeast-Squares Problems\u003C\/li\u003E\n\u003Cli\u003ELinear least Squares (Normal equations, CholeskyQR, SVD-based)\u003C\/li\u003E\n\u003Cli\u003ENon-linear least squares (Gauss-Newton)\u003C\/li\u003E\n\u003Cli\u003EConvexity, Optimality Conditions, and Duality: Convex Functions and Convex Sets, First- and Second-Order Optimality Conditions, Lagrangian, Duality Theory, Gradient methods, Physics Informed neural networks for optimization problems.\u003C\/li\u003E\n\u003Cli\u003EQuantum Approaches for optimization problems: Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), Grover\u2019s search for optimization problems.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EObjectifs d\u0026#039;apprentissage\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003ELearning objectives:\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EAt the end of this course, students will be able to:\u003C\/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EModel\u003C\/strong\u003E scientific and engineering problems as optimization with clear variables, objectives, and constraints.\u003C\/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EClassify\u003C\/strong\u003E problems by smoothness, convexity, and structure (sparsity, separability) and \u003Cstrong\u003Eselect\u003C\/strong\u003E suitable algorithms accordingly.\u003C\/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDerive\u003C\/strong\u003E first- and second-order optimality (KKT) conditions for optimization problems and \u003Cstrong\u003Einterpret\u003C\/strong\u003E them for feasibility and stationarity.\u003C\/li\u003E\n\u003Cli\u003EUse learning approaches for solving large size large-scale scientific problems\u003C\/li\u003E\n\u003Cli\u003EExplain and assess quantum optimization problems.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EOrganisation g\u00e9n\u00e9rale et modalit\u00e9s p\u00e9dagogiques\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003EGeneral course organization and teaching modalities:\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EThe course combines\u0026nbsp;\u003Cstrong\u003Electures\u003C\/strong\u003E\u0026nbsp;introducing theoretical concepts with\u0026nbsp;\u003Cstrong\u003Ehands-on algorithmic applications\u003C\/strong\u003E\u0026nbsp;that allow students to apply these concepts in practice.\u003C\/li\u003E\n\u003Cli\u003EEach 3-hour session blends approximately 80% lecture and 20% application exercises.\u003C\/li\u003E\n\u003Cli\u003EEvaluation is\u0026nbsp;\u003Cstrong\u003Econtinuous\u003C\/strong\u003E, based on\u0026nbsp;\u003Cstrong\u003Emultiple written exercises\u003C\/strong\u003E distributed throughout the course.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EComp\u00e9tences\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003ECompetencies gained in the course:\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003EUpon successful completion, students will have acquired the following competencies:\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EProblem modeling:\u003C\/strong\u003E Translate real-world tasks into convex optimization and games.\u003C\/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAlgorithmic literacy:\u003C\/strong\u003E Understand convexity, relaxations, gradient methods for large-scale problems.\u003C\/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EClassical baselines:\u003C\/strong\u003E Design and implement convex programming and game problems under uncertainty.\u003C\/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EQuantum applications and learning:\u003C\/strong\u003E Understand quantum and learning approaches for optimization and game problems.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EBibliographie\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003EBibliography:\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EJ. Nocedal \u0026amp; S. J. Wright, \u003Cem\u003ENumerical Optimization\u003C\/em\u003E (2nd ed., Springer, 2006).\u003C\/li\u003E\n\u003Cli\u003ES. Boyd \u0026amp; L. Vandenberghe, \u003Cem\u003EConvex Optimization\u003C\/em\u003E (Cambridge Univ. Press, 2004).\u003C\/li\u003E\n\u003Cli\u003EY. Saad, \u003Cem\u003EIterative Methods for Sparse Linear Systems\u003C\/em\u003E (2nd ed., SIAM, 2003).\u003C\/li\u003E\n\u003Cli\u003EG. H. Golub \u0026amp; C. F. Van Loan, \u003Cem\u003EMatrix Computations\u003C\/em\u003E (4th ed., Johns Hopkins, 2013).\u003C\/li\u003E\n\u003Cli\u003EA. Beck, \u003Cem\u003EFirst-Order Methods in Optimization\u003C\/em\u003E (SIAM, 2017).\u003C\/li\u003E\n\u003Cli\u003EAlexander Shapiro, Darinka Dentcheva, Andrzej Ruszczy\u0144ski \u2014 \u003Cem\u003ELectures on Stochastic Programming: Modeling and Theory\u003C\/em\u003E (2nd ed., SIAM\/MPS, 2014)..\u003C\/li\u003E\n\u003Cli\u003EJohn R. Birge, Fran\u00e7ois Louveaux \u2014 \u003Cem\u003EIntroduction to Stochastic Programming\u003C\/em\u003E (2nd ed., Springer, 2011).\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]