Results 11 to 20 of about 7,741 (135)

Second-order subdifferential calculus with applications to tilt stability in optimization [PDF]

open access: yes, 2011
The paper concerns the second-order generalized differentiation theory of variational analysis and new applications of this theory to some problems of constrained optimization in finitedimensional spaces. The main attention is paid to the so-called (full
Mordukhovich, B. S., Rockafellar, R. T.
core   +4 more sources

Directed Subdifferentiable Functions and the Directed Subdifferential without Delta-Convex Structure [PDF]

open access: yes, 2013
We show that the directed subdifferential introduced for differences of convex (delta-convex, DC) functions by Baier and Farkhi can be constructed from the directional derivative without using any information on the DC structure of the function.
Baier, Robert   +2 more
core   +1 more source

Error Estimates for Approximations of Distributed Order Time Fractional Diffusion with Nonsmooth Data [PDF]

open access: yes, 2015
In this work, we consider the numerical solution of an initial boundary value problem for the distributed order time fractional diffusion equation. The model arises in the mathematical modeling of ultra-slow diffusion processes observed in some physical ...
Jin, Bangti   +3 more
core   +2 more sources

Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis [PDF]

open access: yes, 2004
The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications.
Clarenz, Ulrich,   +2 more
core   +2 more sources

KKT reformulation and necessary conditions for optimality in nonsmooth bilevel optimization [PDF]

open access: yes, 2014
For a long time, the bilevel programming problem has essentially been considered as a special case of mathematical programs with equilibrium constraints (MPECs), in particular when the so-called KKT reformulation is in question.
Dempe, Stephan, Zemkoho, Alain B.
core   +1 more source

Lecture notes on the DiPerna-Lions theory in abstract measure spaces [PDF]

open access: yes, 2016
These notes closely correspond to a series of lectures given by the first author in Toulouse, on the recent extension of the theory of ODE well-posedness to abstract spaces, jointly obtained by the two authors.
Ambrosio, Luigi, Trevisan, Dario
core   +3 more sources

Lipschitzian Regularity of the Minimizing Trajectories for Nonlinear Optimal Control Problems

open access: yes, 2002
We consider the Lagrange problem of optimal control with unrestricted controls and address the question: under what conditions we can assure optimal controls are bounded?
Torres, Delfim F. M.
core   +2 more sources

A Corollary for Nonsmooth Systems

open access: yes, 2012
In this note, two generalized corollaries to the LaSalle-Yoshizawa Theorem are presented for nonautonomous systems described by nonlinear differential equations with discontinuous right-hand sides.
Dixon, W. E.   +2 more
core   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

open access: yesAdvanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath   +4 more
wiley   +1 more source

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