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pro vyhledávání: '"49M29"'
In this paper, on the basis of a (Fenchel) duality theory on the continuous level, we derive an $\textit{a posteriori}$ error identity for arbitrary conforming approximations of a primal formulation and a dual formulation of variational problems invo
Externí odkaz:
http://arxiv.org/abs/2410.18780
Motivated by optimal execution with stochastic signals, market impact and constraints in financial markets, and optimal storage management in commodity markets, we formulate and solve an optimal trading problem with a general propagator model under l
Externí odkaz:
http://arxiv.org/abs/2409.12098
Multi-stage decision-making under uncertainty, where decisions are taken under sequentially revealing uncertain problem parameters, is often essential to faithfully model managerial problems. Given the significant computational challenges involved, t
Externí odkaz:
http://arxiv.org/abs/2409.10295
Autor:
Bilenne, Olivier
This note is concerned with the problem of minimizing a separable, convex, composite (smooth and nonsmooth) function subject to linear constraints. We study a randomized block-coordinate interpretation of the Chambolle-Pock primal-dual algorithm, bas
Externí odkaz:
http://arxiv.org/abs/2408.16424
In this paper, on the basis of a (Fenchel) duality theory on the continuous level, we derive an $\textit{a posteriori}$ error identity for arbitrary conforming approximations of the primal formulation and the dual formulation of the scalar Signorini
Externí odkaz:
http://arxiv.org/abs/2407.10912
We introduce an efficient computational framework for solving a class of multi-marginal martingale optimal transport problems, which includes many robust pricing problems of large financial interest. Such problems are typically computationally challe
Externí odkaz:
http://arxiv.org/abs/2406.09959
Geodesic metric spaces support a variety of averaging constructions for given finite sets. Computing such averages has generated extensive interest in diverse disciplines. Here we consider the inverse problem of recognizing computationally whether or
Externí odkaz:
http://arxiv.org/abs/2406.03913
Entropy regularization has been extensively used in policy optimization algorithms to regularize the optimization landscape and accelerate convergence; however, it comes at the cost of introducing an additional regularization bias. This work quantifi
Externí odkaz:
http://arxiv.org/abs/2405.20250
Autor:
Rubbens, Anne, Hendrickx, Julien M.
We propose a novel approach to obtain interpolation constraints for a wide range of function classes, i.e. necessary and sufficient constraints that a set of points, functions values and (sub)gradients must satisfy to ensure the existence of a global
Externí odkaz:
http://arxiv.org/abs/2405.08405
To explore convex optimization on Hadamard spaces, we consider an iteration in the style of a subgradient algorithm. Traditionally, such methods assume that the underlying spaces are manifolds and that the objectives are geodesically convex: the meth
Externí odkaz:
http://arxiv.org/abs/2403.15749