Best Lp approximation with multiple constraints for 1 ⩽ p < ∞

Best Basis Selection for Approximation in Lp

We study the approximation of a function class F in Lp by choosing first a basis B and then using n-term approximation with the elements of B. Into the competition for best bases we enter all greedy (i.e. democratic and unconditional [20]) bases for Lp. We show that if the function class F is well oriented with respect to a particular basis B then, in a certain sense, this basis is the best cho...

Best Simultaneous Approximation in Lp(I, E)

Let G be a reflexive subspace of the Banach space E and let L(I, E) denote the space of all p-Bochner integrable functions on the interval I=[0, 1] with values in E, 1 [ pO.. Given any norm N(· , · ) on R, N nondecreasing in each coordinate on the set R +, we prove that L (I, G) is N-simultaneously proximinal in L(I, E). Other results are also obtained. © 2002 Elsevier Science (USA)

Taylor expansion of best lp-approximations about p = 1

In this paper, we consider the problem of best approximation in e,(n), 1 5 p 5 co. If h,, 1 5 p < co, denotes the best &-approximation of the element h E W " from a proper affine subspace K of W " , h @ K, then limp,1 h p-h* where h; is a best er-approximation of h from K, the i, so-called natural best er-approximation. We prove that, for every T E A, the best &approximations have a Taylor expa...

LP, Weak Constraints, and P-log

LP is a recently introduced formalism that extends answer set programs by adopting the log-linear weight scheme of Markov Logic. This paper investigates the relationships between LP and two other extensions of answer set programs: weak constraints to express a quantitative preference among answer sets, and P-log to incorporate probabilistic uncertainty. We present a translation of LP into progr...

Some Results on p-Best Approximation in Vector Spaces