scholarly journals Auerbach Bases and Minimal Volume Sufficient Enlargements

2011 ◽  
Vol 54 (4) ◽  
pp. 726-738
Author(s):  
M. I. Ostrovskii
Keyword(s):  
Linear Space ◽  
Isometric Embedding ◽  
Normed Linear Space ◽  
Convex Set ◽  
Linear Projection ◽  
Minimal Volume ◽  
Finite Dimensional ◽  
Dimensional Normed Space ◽  
Closed Convex Set

AbstractLet BY denote the unit ball of a normed linear space Y. A symmetric, bounded, closed, convex set A in a finite dimensional normed linear space X is called a sufficient enlargement for X if, for an arbitrary isometric embedding of X into a Banach space Y, there exists a linear projection P: Y → X such that P(BY ) ⊂ A. Each finite dimensional normed space has a minimal-volume sufficient enlargement that is a parallelepiped; some spaces have “exotic” minimal-volume sufficient enlargements. The main result of the paper is a characterization of spaces having “exotic” minimal-volume sufficient enlargements in terms of Auerbach bases.

scholarly journals Super-replication in stochastic volatility models under portfolio constraints

1999 ◽  
Vol 36 (02) ◽  
pp. 523-545 ◽  
Author(s):  
Jakša Cvitanić ◽  
Huyên Pham ◽  
Nizar Touzi
Keyword(s):  
Stochastic Volatility ◽  
Stock Price ◽  
Convex Set ◽  
Short Selling ◽  
Contingent Claims ◽  
Stochastic Volatility Models ◽  
Portfolio Constraints ◽  
Volatility Models ◽  
Closed Convex Set

We study a financial market with incompleteness arising from two sources: stochastic volatility and portfolio constraints. The latter are given in terms of bounds imposed on the borrowing and short-selling of a ‘hedger’ in this market, and can be described by a closed convex set K. We find explicit characterizations of the minimal price needed to super-replicate European-type contingent claims in this framework. The results depend on whether the volatility is bounded away from zero and/or infinity, and also, on if we have linear dynamics for the stock price process, and whether volatility process depends on the stock price. We use a previously known representation of the minimal price as a supremum of the prices in the corresponding shadow markets, and we derive a PDE characterization of that representation.

The graph-theoretic analogue of Tietze's characterization of a convex set and its generalization

1972 ◽  
Vol 72 (1) ◽  
pp. 7-9
Author(s):  
M. D. Guay
Keyword(s):  
Linear Space ◽  
Closed Subset ◽  
Convex Sets ◽  
Convex Set ◽  
Topological Linear Space ◽  
Connected Subset ◽  
Graph Theoretic ◽  
Topological Linear ◽  
Locally Convex

Introduction. One of the most satisfying theorems in the theory of convex sets states that a closed connected subset of a topological linear space which is locally convex is convex. This was first established in En by Tietze and was later extended by other authors (see (3)) to a topological linear space. A generalization of Tietze's theorem which appears in (2) shows if S is a closed subset of a topological linear space such that the set Q of points of local non-convexity of S is of cardinality n < ∞ and S ~ Q is connected, then S is the union of n + 1 or fewer convex sets. (The case n = 0 is Tietze's theorem.)

SEPARATION OF CONVEX SETS IN EXTENDED NORMED SPACES

2015 ◽  
Vol 99 (2) ◽  
pp. 145-165 ◽  
Author(s):  
G. BEER ◽  
J. VANDERWERFF
Keyword(s):  
Convex Sets ◽  
Convex Set ◽  
Separation Theorems ◽  
Normed Spaces ◽  
Finite Dimensional ◽  
Weakly Closed ◽  
Separation Of Convex Sets ◽  
Real Linear ◽  
Closed Convex Set ◽  
Special Case

We give continuous separation theorems for convex sets in a real linear space equipped with a norm that can assume the value infinity. In such a space, it may be impossible to continuously strongly separate a point $p$ from a closed convex set not containing $p$, that is, closed convex sets need not be weakly closed. As a special case, separation in finite-dimensional extended normed spaces is considered at the outset.

scholarly journals Finite Dimensional Chebyshev Subspaces of Lo

2017 ◽  
Vol 22 (1) ◽  
pp. 53
Author(s):  
Aref K. Kamal
Keyword(s):  
Linear Space ◽  
Best Approximation ◽  
Normed Linear Space ◽  
The Best Approximation ◽  
Finite Dimensional

If A is a subset of the normed linear space X, then A is said to be proximinal in X if for each xÎX there is a point y0ÎA such that the distance between x and A; d(x, A) = inf{||x-y||: yÎA}= ||x­-y0||. The element y0 is called a best approximation for x from A. If for each xÎX, the best approximation for x from A is unique then the subset A is called a Chebyshev subset of X.  In this paper the author studies the existence of finite dimensional Chebyshev subspaces of Lo.   

On the existence of support maps with dense images

1977 ◽  
Vol 23 (4) ◽  
pp. 504-510
Author(s):  
Brailey Sims
Keyword(s):  
Linear Space ◽  
Normed Linear Space ◽  
Dense Subset ◽  
Sufficient Conditions ◽  
Complete Answer ◽  
Finite Dimensional

AbstractFor a normed linear space X we investigate conditions for the existence of support maps under which the image of X is a dense subset of the dual. In the case of finite-dimensional spaces a complete answer is given. For more general spaces some sufficient conditions are obtained.

The numerical range of functions and best approximation

1974 ◽  
Vol 76 (1) ◽  
pp. 133-141 ◽  
Author(s):  
Lawrence A. Harris
Keyword(s):  
Convex Hull ◽  
Linear Space ◽  
Best Approximation ◽  
Normed Linear Space ◽  
Numerical Range ◽  
Closed Convex Hull ◽  
Compact Spaces ◽  
Range Of Functions ◽  
Function Mapping

In this note, we state general conditions which imply that the numerical range of a function mapping a set S into a normed linear space Y is the closed convex hull of the spatial numerical range of the function. This conclusion is of interest since, for example, it is equivalent to an extension to non-compact spaces of Kolmogoroff's characterization of functions of best approximation.

scholarly journals The L1-version of the Diliberto–Straus algorithm in C(T × S)

1984 ◽  
Vol 27 (1) ◽  
pp. 31-45 ◽  
Author(s):  
W. A. Light ◽  
S. M. Holland
Keyword(s):  
Linear Space ◽  
Best Approximation ◽  
Measure Space ◽  
Normed Linear Space ◽  
Finite Measure ◽  
Hausdorff Spaces ◽  
Chebyshev Subspace ◽  
Borel Measures ◽  
Finite Dimensional ◽  
Measure Spaces

Let (S, Σ, μ) and (T, Θ, v) be two measure spaces of finite measure where we assume S, T are compact Hausdorff spaces and μ, v are regular Borel measures. We construct the product measure space (T x S, >, Φ σ) in the usual way. Let G = [gl, g2, …, gp] and H = [hl, h1, …, hm be finite dimensional subspaces of C(S) and C(T) respectivelywhere G and H are also Chebyshev with respect to the L1-norm. Note that a subspace Y of a normed linear space X is Chebyshev if each x ∈X possesses exactly one best approximation y ∈Y. For example, in C(S) with the L1-norm, the subspace of polynomials of degree at most n is a Chebyshev subspace. This is an old theorem of Jackson. Now set

scholarly journals Super-replication in stochastic volatility models under portfolio constraints

1999 ◽  
Vol 36 (2) ◽  
pp. 523-545 ◽  
Author(s):  
Jakša Cvitanić ◽  
Huyên Pham ◽  
Nizar Touzi
Keyword(s):  
Stochastic Volatility ◽  
Stock Price ◽  
Convex Set ◽  
Short Selling ◽  
Contingent Claims ◽  
Stochastic Volatility Models ◽  
Portfolio Constraints ◽  
Volatility Models ◽  
Closed Convex Set

We study a financial market with incompleteness arising from two sources: stochastic volatility and portfolio constraints. The latter are given in terms of bounds imposed on the borrowing and short-selling of a ‘hedger’ in this market, and can be described by a closed convex set K. We find explicit characterizations of the minimal price needed to super-replicate European-type contingent claims in this framework. The results depend on whether the volatility is bounded away from zero and/or infinity, and also, on if we have linear dynamics for the stock price process, and whether volatility process depends on the stock price. We use a previously known representation of the minimal price as a supremum of the prices in the corresponding shadow markets, and we derive a PDE characterization of that representation.

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