98%
921
2 minutes
20
It has long been postulated that within density-functional theory (DFT), the total energy of a finite electronic system is convex with respect to electron count so that 2Ev[N0] ≤ Ev[N0 - 1] + Ev[N0 + 1]. Using the infinite-separation-limit technique, this Communication proves the convexity condition for any formulation of DFT that is (1) exact for all v-representable densities, (2) size-consistent, and (3) translationally invariant. An analogous result is also proven for one-body reduced density matrix functional theory. While there are known DFT formulations in which the ground state is not always accessible, indicating that convexity does not hold in such cases, this proof, nonetheless, confirms a stringent constraint on the exact exchange-correlation functional. We also provide sufficient conditions for convexity in approximate DFT, which could aid in the development of density-functional approximations. This result lifts a standing assumption in the proof of the piecewise linearity condition with respect to electron count, which has proven central to understanding the Kohn-Sham bandgap and the exchange-correlation derivative discontinuity of DFT.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1063/5.0174159 | DOI Listing |
IEEE Trans Neural Netw Learn Syst
September 2025
In essence, reinforcement learning (RL) solves optimal control problem (OCP) by employing a neural network (NN) to fit the optimal policy from state to action. The accuracy of policy approximation is often very low in complex control tasks, leading to unsatisfactory control performance compared with online optimal controllers. A primary reason is that the landscape of value function is always not only rugged in most areas but also flat on the bottom, which damages the convergence to the minimum point.
View Article and Find Full Text PDFSurv Ophthalmol
September 2025
Paris Cité University, Department of Ophthalmology, Lariboisière University Hospital, APHP, F-75010 Paris, France.
Dome-shaped macula (DSM) is a distinctive anatomical entity characterized by an inward convexity of the macula, initially described in highly myopic eyes within posterior staphyloma, but it is now recognized as occurring across a broader spectrum of refractive conditions, including mild myopia and even emmetropia. Since its initial description in 2008, advances in imaging technologies and longitudinal studies have significantly improved our understanding of DSM. This review analyzed the recent literature, focusing on publications from the last 10 years.
View Article and Find Full Text PDFJ Optim Theory Appl
September 2025
Department of Mathematics, Linköping University, SE-581 83 Linköping, Sweden.
Single-level reformulations of (nonconvex) distributionally robust optimization (DRO) problems are often intractable, as they contain semi-infinite dual constraints. Based on such a semi-infinite reformulation, we present a safe approximation that allows for the computation of feasible solutions for DROs that depend on nonconvex multivariate simple functions. Moreover, the approximation allows to address ambiguity sets that can incorporate information on moments as well as confidence sets.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
September 2025
This article presents an accelerated distributed optimization algorithm for online optimization problems over large-scale networks. The proposed algorithm's iteration only relies on local computation and communication. To effectively adapt to dynamic changes and achieve a fast convergence rate while maintaining good convergence performance, we design a new algorithm called NGTAdam.
View Article and Find Full Text PDFJ Optim Theory Appl
August 2025
Department of Economics, Universitá degli Studi dell'Insubria, Varese, Italy.
We propose a novel concept of robustness grounded in the framework of set-valued probabilities, offering a unified and versatile approach to tackling challenges associated with the statistical estimation of uncertain or unknown probabilities. By employing scalarization techniques for set-valued probabilities, we derive optimality conditions. Additionally, we establish generalized convexity properties and stability conditions, which further underpin the robustness of our approach.
View Article and Find Full Text PDF