For each countable ordinal α, let Sα be the Schreier set of order α and XSα corresponding space α. In this paper, we prove several new properties these spaces. 1. If is nonzero, then XSαpossesses λ-property Aron Lohman a (V)-polyhedral in sense Fonf Vesely. 2. nonzero 1<p<∞, p-convexification p possesses uniform Lohman. 3. ∗ has λ-property. 4. n∈N, if T:XSn→XSnis an onto linear isometry, Tei=±e...

Energy hubs, which integrate multiple energy vectors through converters, can enhance the value of Integrated Local Systems (ILES) via increased flexibility and reduced costs. However, uncertain renewable non-convex, non-linear properties flows complicate modelling operation hub systems. This paper develops chance-constrained optimization methods for planning systems under uncertainty. The formu...

We consider the problem of reconstructing two-dimensional convex binary matrices from their row and column sums with adjacent ones. Instead of requiring the ones to occur consecutively in each row and column, we maximize the number of adjacent ones. We reformulate the problem by using integer programming and we develop approximate solutions based on linearization and convexification techniques.

This paper presents the design and implementation of a low power 8 × 8 2-D DCT chip based on a computation-effective algorithm. Computational complexity can be reduced by simplifying the direct 2-D algorithm. Thus, the low power consumption is achieved due to complexity reduction. Besides, the parallel distributedarithmetic (DA) technique is used to realize constant multiplication due to the lo...

By virtue of convexification techniques, we study best approximations to a closed set C in a Hilbert space as well as perturbation conditions relative to C and a nonlinear inequality system. Some results on equivalence of the best approximation and the basic constraint qualification are established.

Let (QP ) be a 0-1 quadratic program which consists in minimizing a quadratic function subject to linear constraints. In this paper, we present a general method to solve (QP ) by reformulation of the problem into an equivalent 0-1 program with a convex quadratic objective function, followed by the use of a standard mixed integer quadratic programming solver. Our convexification method, which is...