Publication
Approximate dynamic programming via sum of squares programming
Publications associées (129)
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Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...
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Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...
EPFL2023A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling
In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...
2023Dynamically Orthogonal Approximation for Stochastic Differential Equations
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In this paper, we set the mathematical foundations of the Dynamical Low Rank Approximation (DLRA) method for high-dimensional stochastic differential equations. DLRA aims at approximating the solution as a linear combination of a small number of basis vect ...
2023On The Convergence Of Stochastic Primal-Dual Hybrid Gradient
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In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution with convexity and linear convergenc ...
SIAM PUBLICATIONS2022Quadratic serendipity element shape functions on general planar polygons
This paper proposes a method for the construction of quadratic serendipity element (QSE) shape functions on planar convex and concave polygons. Existing approaches for constructing QSE shape functions are linear combinations of the pair-wise products of ge ...
ELSEVIER SCIENCE SA2022A convex set of robust D—stabilizing controllers using Cauchy's argument principle
Alireza Karimi, Philippe Louis Schuchert
A new approach is presented to obtain a convex set of robust D—stabilizing fixed structure controllers, relying on Cauchy's argument principle. A convex set of D—stabilizing controllers around an initial D—stabilizing controller for a multi-model set is re ...
Elsevier2022Proximal Point Imitation Learning
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This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence assumptions. We begin with the minimax formulation of the problem and then o ...
2022A smooth basis for atomistic machine learning
Michele Ceriotti, Kevin Kazuki Huguenin-Dumittan, Filippo Bigi
Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighborhood of each atom in the system. Symmetry considerations support the use of spherical harmonics to ...
AIP Publishing2022A first-order primal-dual method with adaptivity to local smoothness
Volkan Cevher, Maria-Luiza Vladarean
We consider the problem of finding a saddle point for the convex-concave objective , where is a convex function with locally Lipschitz gradient and is convex and possibly non-smooth. We propose an ...
2021