Page 144 - DMTH502_LINEAR_ALGEBRA
P. 144

Linear Algebra




                    Notes          These polynomial functions are easily shown to be
                                                             (x t  )(x t  )
                                                     p (x) =    2     3
                                                      1
                                                            (t  t  )(t  t  )
                                                             1  2  1  3
                                                             (x t 1 )(x t 3 )
                                                     p (x) =
                                                      2     (t  2  t 1 )(t 2  t 3 )

                                                             (x t  )(x t  )
                                                     p (x) =    1     2
                                                      3
                                                            (t 3  t 1 )(t 3  t 2  )
                                   The basis {p , p , p } for V is interesting, because according to (7) we have to each p in V.
                                            1  2  3
                                                                            t
                                                                              p
                                                                     t
                                                       p = p ( )p 1  p ( )p 2  p ( ) .
                                                              t
                                                                            3
                                                                     2
                                                              1
                                                                               3
                                   Thus, if c , c  and c  are any real numbers, there is exactly one polynomial function  p over R
                                          1  2    3
                                   which has  degree at  most 2 and satisfies  ( )p t j  c j  , j  1,2,3.   This polynomial function  is
                                   p = c p  + c p  + c p .
                                      1  1  2  2  3  3
                                   Now let us discuss the relationship between linear functionals and subspaces. If f is a non-zero
                                   linear functional, then the rank of f is 1 because the range of f  is a non-zero subspace of  the scalar
                                   field and must (therefore) be the scalar field. If the underlying space V is finite-dimensional, the
                                   rank plus nullity theorem tells us that the null space N  has dimension
                                                                               f
                                                   dim N = dim V – 1.
                                                        f
                                   In a vector space of dimension n, a subspace of dimension n – 1 is called a hyperspace. Such
                                   spaces are sometimes called hyperplanes or subspaces of co-dimension 1. Is every hyperspace
                                   the null space of a linear functional? The answer is easily seem to be yes. It is not much more
                                   difficult to show that each d-dimensional subspace of an n-dimensional space is the intersection
                                   of the null spaces of (n – d) linear functionals (Theorem below).
                                   Definition: If V is a vector space over the field F and S is a subset of V, the annihilator of S is the
                                   set S° of linear functionals on V such that f( ) = 0 for every   in S.
                                   It should be clear that S° is a subspace of V*, whether S is a subspace of V or not. If S is the set
                                   consisting of the zero vector alone, then S° = V*. If S = V, then S° is the zero subspace of V*. (This
                                   is easy to see when V is finite-dimensional.)

                                   Theorem 3. Let V be a finite-dimensional vector space over the field F, and let W be a subspace of
                                   V. Then
                                           din W + dim W° = dim V.

                                   Proof: Let k be the dimension of W and { , ...,  } a basis for W. Choose vector   , ...,   , in V
                                                                    1    k                         k + 1  n
                                   such that { ,...,  } is a basis for V. Let {f , ..., f } be the basis for V* which is dual to this basis for
                                            1   n                  1   n
                                   V.
                                                       f
                                   This claim is that  { f k  1 ,... }  is a basis for the annihilator W°. Certainly f  belongs to W° for i   k
                                                       n
                                                                                            i
                                   + 1, because
                                                     ( f  ) =
                                                    i  i     ij
                                   and   ij  0  if i   k + 1 and j   k; from this it follows that, for i   k + 1,  ( ) 0f i   whenever   is a
                                   linear combination of   1 ,...,  k .  The functionals  f  k  1 ,..., f  are independent, so all we must show
                                                                               n
                                   is that they span W°. Suppose f is in V*.





          138                               LOVELY PROFESSIONAL UNIVERSITY
   139   140   141   142   143   144   145   146   147   148   149