Preliminary.

Slides:



Advertisements
Similar presentations
Ball Separation Properties in Banach Spaces Sudeshna Basu Integration, Vector Measure and Related Topics VI Bedlewo, June
Advertisements

Network Systems Lab. Korea Advanced Institute of Science and Technology No.1 Some useful Contraction Mappings  Results for a particular choice of norms.
Chaper 3 Weak Topologies. Reflexive Space.Separabe Space. Uniform Convex Spaces.
1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.
Symmetric Matrices and Quadratic Forms
Chapter 5 Orthogonality
3.II.1. Representing Linear Maps with Matrices 3.II.2. Any Matrix Represents a Linear Map 3.II. Computing Linear Maps.
Lecture 1 Linear Variational Problems (Part I). 1. Motivation For those participants wondering why we start a course dedicated to nonlinear problems by.
3.II. Homomorphisms 3.II.1. Definition 3.II.2. Range Space and Nullspace.
Chapter 4 Linear Transformations. Outlines Definition and Examples Matrix Representation of linear transformation Similarity.
I. Isomorphisms II. Homomorphisms III. Computing Linear Maps IV. Matrix Operations V. Change of Basis VI. Projection Topics: Line of Best Fit Geometry.
Preliminaries/ Chapter 1: Introduction. Definitions: from Abstract to Linear Algebra.
Orthogonality and Least Squares
6 6.3 © 2012 Pearson Education, Inc. Orthogonality and Least Squares ORTHOGONAL PROJECTIONS.
App III. Group Algebra & Reduction of Regular Representations 1. Group Algebra 2. Left Ideals, Projection Operators 3. Idempotents 4. Complete Reduction.
Mathematics1 Mathematics 1 Applied Informatics Štefan BEREŽNÝ.
Example: Introduction to Krylov Subspace Methods DEF: Krylov sequence
CHAPTER FIVE Orthogonality Why orthogonal? Least square problem Accuracy of Numerical computation.
Chapter 5 Orthogonality.
Chapter 2: Vector spaces
AN ORTHOGONAL PROJECTION
Chapter 3 L p -space. Preliminaries on measure and integration.
Chap. 6 Linear Transformations
Orthogonality and Least Squares
Three different ways There are three different ways to show that ρ(A) is a simple eigenvalue of an irreducible nonnegative matrix A:
Chapter 2 Nonnegative Matrices. 2-1 Introduction.
MAT 4725 Numerical Analysis Section 7.1 (Part II) Norms of Vectors and Matrices
MAT 3749 Introduction to Analysis Section 1.3 Part I Countable Sets
MA5241 Lecture 1 TO BE COMPLETED
Abstract matrix spaces and their generalisation Orawan Tripak Joint work with Martin Lindsay.
I.3 Introduction to the theory of convex conjugated function.
4.8 Rank Rank enables one to relate matrices to vectors, and vice versa. Definition Let A be an m  n matrix. The rows of A may be viewed as row vectors.
Chap. 5 Inner Product Spaces 5.1 Length and Dot Product in R n 5.2 Inner Product Spaces 5.3 Orthonormal Bases: Gram-Schmidt Process 5.4 Mathematical Models.
Chapter 4 Hilbert Space. 4.1 Inner product space.
Lemma II.1 (Baire) Let X be a complete metric space and a seq. of closed sets. Assume that for each n. Then.
CS Lecture 14 Powerful Tools     !. Build your toolbox of abstract structures and concepts. Know the capacities and limits of each tool.
Preliminaries 1. Zorn’s Lemma Relation S: an arbitary set R SXS R is called a relation on S.
Linear Programming Back to Cone  Motivation: From the proof of Affine Minkowski, we can see that if we know generators of a polyhedral cone, they.
MAT 4725 Numerical Analysis Section 7.1 Part I Norms of Vectors and Matrices
4 Vector Spaces 4.1 Vector Spaces and Subspaces 4.2 Null Spaces, Column Spaces, and Linear Transformations 4.3 Linearly Independent Sets; Bases 4.4 Coordinate.
Let W be a subspace of R n, y any vector in R n, and the orthogonal projection of y onto W. …
Vector Space Examples Definition of vector space
Theorem of Banach stainhaus and of Closed Graph
Proving that a Valid Inequality is Facet-defining
Section 4.1: Vector Spaces and Subspaces
Section 4.1: Vector Spaces and Subspaces
Polyhedron Here, we derive a representation of polyhedron and see the properties of the generators. We also see how to identify the generators. The results.
Polyhedron Here, we derive a representation of polyhedron and see the properties of the generators. We also see how to identify the generators. The results.
Orthogonality and Least Squares
§1-3 Solution of a Dynamical Equation
VII.1 Hille-Yosida Theorem
Linear Algebra Lecture 40.
Theorems about LINEAR MAPPINGS.
Symmetric Matrices and Quadratic Forms
Affine Spaces Def: Suppose
I.4 Polyhedral Theory (NW)
Linear Algebra Lecture 23.
Back to Cone Motivation: From the proof of Affine Minkowski, we can see that if we know generators of a polyhedral cone, they can be used to describe.
I.4 Polyhedral Theory.
Proving that a Valid Inequality is Facet-defining
Linear Algebra Lecture 41.
VI.3 Spectrum of compact operators
(Convex) Cones Def: closed under nonnegative linear combinations, i.e.
Vector Spaces, Subspaces
Preliminaries/ Chapter 1: Introduction
Preliminaries on normed vector space
Calculus In Infinite dimensional spaces
Orthogonality and Least Squares
8/7/2019 Berhanu G (Dr) 1 Chapter 3 Convex Functions and Separation Theorems In this chapter we focus mainly on Convex functions and their properties in.
Presentation transcript:

Preliminary

Theorem I.1 Hahn-Banach, analytic form

Minkowski gauge Theorem Let K be a convex set in E with 0 being its interior point. Define a function

Proof of Minkowski gauge Theorem p.1

Proof of Minkowski gauge Theorem p.2

Minkowski gauge function of K is called the Minkowski gauge function of K

Lemma 1

Proof of Lemma 1 p.1

Proof of Lemma 1 p.2

Remark

Proof of Remark p.1

Hyperplane E:real vector space is called a Hyperplane of equation[f=α] If α=0, then H is a Hypersubspace

Proposition 1.5 E: real normed vector space The Hyperplane [f=α] is closed if and only if

Theorem 1.6(Hahn-Banach; the first geometric form) E:real normed vector space Let be two disjoint nonnempty convex sets. Suppose A is open, then there is a closed Hyperplane separating A and B in broad sense.

Epigraph E : set Epigraph, is the set

Conjugated function Assume that E is a real n.v.s Given such that Define the conjugated function of by

Theorem I.11 see next page Suppose and are convex and suppose that there is such that and is continuous at

Observe (1) usually appears for constrain (2) see next page

The proof of Thm I.11 see next page

Application of Thm I.11 Let be nonempty, close and convex. Put

Let

Application of Hahn-Banach Theorem E: real normed vector space G: vector subspace Then for any

Theorem II.5 (Open Mapping Thm,Banach) Let E and F be two Banach spaces and T a surjective linear continuous from E onto F. Then there is a constant c>0 such that

Theorem II.8 Let E be a Banach space and let G and L be two closed vector subspaces such that G+L is closed . Then there exists constant such that

(13) any element z of G+L admits a decomposition of the form z=x+y with L x G z y

Corollary II.9 Let E be a Banach space and let G and L be two closed vector subspaces such that G+L is closed . Then there exists constant such that

(14) L G x

II.5 Orthogonolity Relation

are closed vector subspace X: Banach space : vector subspace orthogonal of M : vector subspace Let are closed vector subspace of X and X’ ,respectively.

Proposition II.12 Suppose M is a vector subspace of X then If N is a vector subspace of then

Proposition II.13 Suppose G and L are closed vector subspaces of X. We have (16) (17)

Corollary II.14 Suppose G and L are closed vector subspaces of X. We have (18) (19)

Proposition II.15 p.1 Suppose G and L are closed vector subspaces of X. The following properties are equivalent:

Proposition II.15 p.2 (a) G+L is closed in X. (b) is closed in X´ (c)

II.5 Orthogonolity Relation

Theorem I.11 see next page Suppose and are convex and suppose that there is such that and is continuous at