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Extended real-valued functions are often used in optimization theory, but in different ways for infimum problems and for supremum problems. We present an approach to extended real-valued functions that works for all types of problems and…

Optimization and Control · Mathematics 2018-06-11 Petra Weidner

Reference frame optimization is a generic framework to calculate a spatially-varying observer field that views an unsteady fluid flow in a reference frame that is as-steady-as-possible. In this paper, we show that the optimized vector field…

Fluid Dynamics · Physics 2021-11-03 Holger Theisel , Markus Hadwiger , Peter Rautek , Thomas Theußl , Tobias Günther

The Easy Path Wavelet Transform is an adaptive transform for bivariate functions (in particular natural images) which has been proposed in [1]. It provides a sparse representation by finding a path in the domain of the function leveraging…

Information Theory · Computer Science 2017-02-16 Renato Budinich

We show that the core reasons that complex and hypercomplex valued neural networks offer improvements over their real-valued counterparts is the weight sharing mechanism and treating multidimensional data as a single entity. Their algebra…

Neural and Evolutionary Computing · Computer Science 2020-09-10 Chase J Gaudet , Anthony S Maida

The celebrated Expected Shortfall (ES) optimization formula implies that ES at a fixed probability level is the minimum of a linear real function plus a scaled mean excess function. We establish a reverse ES optimization formula, which says…

Risk Management · Quantitative Finance 2023-05-23 Yuanying Guan , Zhanyi Jiao , Ruodu Wang

Variational convexity, together with ist strong counterpart, of extended-real-valued functions has been recently introduced by Rockafellar. In this paper we present second-order characterizations of these properties, i.e., conditions using…

Optimization and Control · Mathematics 2025-02-04 Helmut Gfrerer

Recognizing elementary underlying concepts from observations (disentanglement) and generating novel combinations of these concepts (compositional generalization) are fundamental abilities for humans to support rapid knowledge learning and…

Computer Vision and Pattern Recognition · Computer Science 2023-05-30 Tao Yang , Yuwang Wang , Cuiling Lan , Yan Lu , Nanning Zheng

In this paper, stability and sensitivity properties of a class of parametric constrained optimization problem, whose feasible region is defined by a set-valued inclusion, are investigated through the associated optimal value function.…

Optimization and Control · Mathematics 2024-12-06 Amos Uderzo

Variational principles in mechanics, field theory and geometric analysis are usually formulated on closed admissible classes, where boundary variations are either fixed or independently cancelled through natural boundary conditions.…

Classical Physics · Physics 2026-05-19 Francisco Monroy

We prove the applicability of the Weighted Energy-Dissipation (WED) variational principle [50] to nonlinear parabolic stochastic partial differential equations in abstract form. The WED principle consists in the minimization of a…

Optimization and Control · Mathematics 2021-01-19 Luca Scarpa , Ulisse Stefanelli

The AAV effect is the well-known phenomenon where a weak measurement followed by post-selection leads to a pointer shift proportional to the weak value of the measured observable. The effect is usually derived by considering a perturbative…

Quantum Physics · Physics 2014-06-04 Jeff Z. Salvail

Inverse optimization (Inverse optimal control) is the task of imputing a cost function such that given test points (trajectories) are (nearly) optimal with respect to the discovered cost. Prior methods in inverse optimization assume that…

Optimization and Control · Mathematics 2025-10-21 Filip Bečanović , Jared Miller , Vincent Bonnet , Kosta Jovanović , Samer Mohammed

We consider the stochastic variational inequality problem in which the map is expectation-valued in a component-wise sense. Much of the available convergence theory and rate statements for stochastic approximation schemes are limited to…

Optimization and Control · Mathematics 2019-11-25 Aswin Kannan , Uday V. Shanbhag

The control variates (CV) method is widely used in policy gradient estimation to reduce the variance of the gradient estimators in practice. A control variate is applied by subtracting a baseline function from the state-action value…

Machine Learning · Computer Science 2021-08-12 Yuanyi Zhong , Yuan Zhou , Jian Peng

This paper considers variational inequalities (VI) defined by the conditional value-at-risk (CVaR) of uncertain functions and provides three stochastic approximation schemes to solve them. All methods use an empirical estimate of the CVaR…

Optimization and Control · Mathematics 2022-11-16 Jasper Verbree , Ashish Cherukuri

The main aim of this note is to point out by means of counter-examples that some arguments of the proofs of two theorems about a "half variational principle" for multivalued maps, formulated recently by Vivas and Sirvent [Metric entropy for…

Dynamical Systems · Mathematics 2024-06-25 Jan Andres , Pavel Ludvík

The realization of a standard Adaptive Finite Element Method (AFEM) preserves the mesh conformity by performing a completion step in the refinement loop: in addition to elements marked for refinement due to their contribution to the global…

Numerical Analysis · Mathematics 2024-02-15 Claudio Canuto , Davide Fassino

We study an extremal projection principle for families of operators ordered by domination, induced by fixed bounded linear mappings acting on a source with an additive baseline. Stability is defined through domination of second--order…

Functional Analysis · Mathematics 2026-02-04 Philip Kennerberg

A partially ordered set is r-thick if every nonempty open interval contains at least r elements. This paper studies the flag vectors of graded, r-thick posets and shows the smallest convex cone containing them is isomorphic to the cone of…

Combinatorics · Mathematics 2007-05-23 Margaret M. Bayer , Gabor Hetyei

The empirical mode decomposition (EMD) has achieved its reputation by providing a multi-scale time-frequency representation of nonlinear and/or nonstationary signals. To extend this method to vector-valued signals (VvS) in multidimensional…

Numerical Analysis · Mathematics 2015-02-25 Boqiang Huang , Angela Kunoth
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