convex sets
Projection onto Convex Sets Method in Spacefrequency Domain for Super Resolution
... The principle of the POCS method is simple and direct to view. It uses a priori knowledge of the spatial response characteristics of the imaging system. In the process of reconstructing HR image, there are many ...
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General Reflexivity For Absolutely Convex Sets, Mahtab Lak
... Suppose (X, Y, E) is a reflexivity triple and A ⊂ X is ac-E-reflexive. We say that A is heredi- tarily ac-E-reflexive if and only if every σ (X, Y )-closed absolutely convex subset of A is ac-E- reflexive. For ...
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Locating Multiple Facilities in Convex Sets with Fuzzy Data and Block Norms
... In Section 2 we introduce a model for locating multi- ple facilities in convex sets. In Section 3 we give a prac- tical exampl. A method for fuzzy linear programming problems and locating for fuzzy data are ...
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On hyperspaces of max-plus and max-min convex sets
... max-min convex sets. They are counterparts of the convex sets in the idempotent mathematics, ...max-plus convex sets as well as survey of results in max-plus convexity can be ...
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Remarks on Separation of Convex Sets, Fixed-Point Theorem, and Applications in Theory of Linear Operators
... of convex sets in a real reflexive Banach space are investigated, existence of a fixed-point theorem for a general mapping acting in a Banach space and the obtained results are applied to study certain ...
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Fitting Convex Sets to Data: Algorithms and Applications
... Informed choices of the lifting dimension q. In many settings in practice, a suitable choice of the lifting dimension q is usually not known in advance. Smaller values of q allow us to obtain more concisely described ...
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On geodesic strongly E convex sets and geodesic strongly E convex functions
... of sets on Riemannian man- ifolds and a new class of functions defined on them, which are called geodesic strongly E-convex sets and geodesic strongly E-convex functions, and some of their ...
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Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
... of convex set)—for non- negative ...data sets show that TLFre and DPC improve the efficiency of SGL and nonnegative Lasso by several orders of ...Decomposition, Convex Sets, Composite Function ...
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Optimal Separation of Twin Convex Sets under Externalities
... This paper studies the outcomes of independent and interdependent pair-wise contests between economic agents subject to an optimal external decision problem for each pair. The external deci- sion maker like the ...
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Implementation of a projection onto convex sets iteration based image coder
... On the other hand, if the intersection is empty, the POCS method will find the closest image, described by the selected number of coefficients, to the image to be encoded considering onl[r] ...
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Notes on Convex sets, Polytopes, Polyhedra, Combinatorial Topology, Voronoi Diagrams and Delaunay Triangulations - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... #port type protocol wait user program program arguments ftp stream tcp nowait root /usr/sbin/tcpd /usr/sbin/proftpd pop3 stream tcp nowait root /usr/sbin/tcpd /us[r] ...
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Convex sets and inequalities
... Given a natural correspondence between a family of inequalities and a closed convex set in a topological linear space, one might expect that an inequality corresponding to a special point (e.g., an extreme point) ...
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Functionally closed sets and functionally convex sets in real Banach spaces
... approximately convex subset M of a linear normed space X, by denoting the multivalued mapping which puts into correspondence with each point x ∈ X, the set T x of all points y ∈ M which satisfy the condition d(x, ...
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LNC points for m convex sets
... Techniques identical to those employed in the proof of Theorem 2 may be used to obtain the following result... Inc points of.[r] ...
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An Online Convex Optimization Approach to Blackwell's Approachability
... For convex target sets, these vectors are obtained as projections from the current average payoff vector to the ...target sets that are convex cones, and then generalized to any convex ...
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Dykstra’s Algorithm for the Optimal Approximate Symmetric Positive Semidefinite Solution of a Class of Matrix Equations
... In this paper, we propose a new algorithm to compute the optimal approximate symmetric positive semidefinite solution of Equation (1.3). We state Problem I as the minimization of a convex quadratic function over ...
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On the Convex Feasibility Problem
... The paper is organized as follows. In section 2 we briefly state the convergence properties of the Mann iteration process. The projection algorithm is a particular case of this iteration, so that its convergence results ...
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Some fixed point theorems in locally p-convex spaces
... In nonlinear analysis, one of the dynamic research areas is investigation of existence of a fixed point of maps on convex sets and p-convex sets. Recently, a number of fixed point theorems have ...
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An improved method for solving multiple-sets split feasibility problem
... Throughout this paper, let H be a Hilbert space, ·, · denote the inner product and · de- note the corresponding norm. The multiple-sets split feasibility problem (MSSFP) is a gen- eralization of the split ...
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Solving nonlinear programming problems with unbounded non convex constraint sets via a globally convergent algorithm
... to extend the results in [, ] to more general non-convex sets. However, there are no re- sults reported in the literature about the work in [] extended to unbounded non-convex sets. In ...
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