Abstract convex optimal antiderivatives

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

How to compute antiderivatives

The roots of this problem go back to the beginnings of calculus and it is even sometimes called “Newton’s problem”. Historically, it has played a major role in the development of the theory of the integral. For example, it was Lebesgue’s primary motivation behind his theory of measure and integration. Indeed, the Lebesgue integral solves the primitive problem for the important special case when...

متن کامل

Antiderivatives for Complex Functions

Since we have the same product rule, quotient rule, sum rule, chain rule etc. available to us for differentiating complex functions, we already know many antiderivatives. For example, by differentiating f (z) = z one obtains f ′(z) = nzn−1, and from this one sees that the antiderivative of z is 1 n+1 z – except for the very important case where n = −1. Of course that special case is very import...

متن کامل

Abstract interpretation meets convex optimization

Interpretation Meets Convex Optimization ? Thomas Martin Gawlitza, Helmut Seidl, Assalé Adjé, Stephane Gaubert, and Eric Goubault 1 CNRS/VERIMAG, France [email protected] 2 Technische Universität München, Germany [email protected] 3 CEA, LIST and LIX, Ecole Polytechnique (MeASI) [email protected] 4 INRIA Saclay and CMAP, Ecole Polytechnique, F-91128 Palaiseau Cedex, France Stephane.Gaubert...

متن کامل

Regularizing the Abstract Convex Program

fi = inf( p(x) : g(x) E 4, x E R 1, P) where S is an arbitrary convex cone in a finite dimensional space, R is a convex set, and p and g are respectively convex and S-convex (on a), were given in [lo]. These characterizations hold without any constraint qualification. They use the “minimal cone” .S’ of (P) and the cone of directions of constancy D;(S’). In the faithfully convex case these cones...

متن کامل

Convex Optimal Uncertainty Quantification

Optimal uncertainty quantification (OUQ) is a framework for numerical extreme-case analysis of stochastic systems with imperfect knowledge of the underlying probability distribution. This paper presents sufficient conditions under which an OUQ problem can be reformulated as a finite-dimensional convex optimization problem, for which efficient numerical solutions can be obtained. The sufficient ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Annales de l'Institut Henri Poincaré C, Analyse non linéaire

سال: 2012

ISSN: 0294-1449

DOI: 10.1016/j.anihpc.2012.01.004