In this paper, we provide a new scheme for approximating the weakly efficient solution set class of vector optimization problems with rational objectives over feasible defined by finitely many polynomial inequalities. More precisely, present procedure to obtain sequence explicit approximations problem in question. Each approximation is intersection sublevel single and set. To end, make use achi...

The aim of this paper is to provide a rigorous variational formulation for the detection of points in 2-d biological images. To this purpose we introduce a new functional whose minimizers give the points we want to detect. Then we define an approximating sequence of functionals for which we prove the Γ-convergence to the initial one. AMS 2000 subject Classification: 49J45 49Q20

A lattice-type regularization of the supersymmetric eld theories on a supersphere is constructed by approximating the ring of scalar superrelds by an integer-valued sequence of nite dimensional rings of supermatrices and using the diierencial calculus of non-commutative geometry. The regulated theory involves only nite number of degrees of freedom and is manifestly supersymmetric.

The Mα energy which is usually minimized in branched transport problems among singular 1-dimensional rectifiable vector measures with prescribed divergence is approximated (and convergence is proved) by means of a sequence of elliptic energies, defined on more regular vector fields. The procedure recalls the Modica-Mortola one for approximating the perimeter, and the double-well potential is re...

In this paper we prove strong convergence theorems for approximating the fixed point of Lipschitzian semigroup and infinite family of nonexpansive mappings with respect to finite family of sequence {μi,n} ∞ i=1,n=1 of left strong regular invariant means and Meir-Keeler type contraction in uniformly convex and smooth Banach spaces. Our result extend and improve many recent results.

We tackle the issue of finding a good policy when the number of policy updates is limited. This is done by approximating the expected policy reward as a sequence of concave lower bounds which can be efficiently maximized, drastically reducing the number of policy updates required to achieve good performance. We also extend existing methods to negative rewards, enabling the use of control variates.

Let K be a closed convex subset of a Hilbert space H and T : K ⊸ K a nonexpansive multivalued map with a unique fixed point z such that {z} = T (z). It is shown that we can construct a sequence of approximating fixed points sets converging in the sense of Mosco to z.