.
One has 
.
Taking the square root of both sides gives the result.
Equality holds precisely when one is a nonnegative multiple of the other. This is a consequence of the analogous assertion of the next problem.
.  When does equality hold?
The first assertion is the triangle inequality. I claim that equality holds precisely when one vector is a non-positive multiple of the other.
If 
 for some real 
, then substituting shows that the inequality
is equivalent to 
 and clearly equality holds if
a is non-positive.  Similarly, one has equality if 
 for some real 
.
Conversely, if equality holds, then 
,
and so 
.  By Theorem 1-1 (2), it follows that 
 and 
are linearly dependent.  If 
 for some real 
, then substituting
back into the equality shows that 
 must be non-positive or 
 must be 0.
The case where 
 is treated similarly.
.
If 
, then the inequality to be proved is just 
which is just the triangle inequality.  On the other hand, if 
,
then the result follows from the first case by swapping the roles of 
 and
.
 is called the distance between 
 and 
.
Prove and interpret geometrically the ``triangle inequality": 
.
The inequality follows from Theorem 1-1(3):
Geometrically, if 
, 
, and 
 are the vertices of a triangle, then the
inequality says that the length of a side is no larger than the sum of the
lengths of the other two sides.
 and 
 be functions integrable on 
.
.
Theorem 1-1(2) implies the inequality of Riemann sums:
Taking the limit as the mesh approaches 0, one gets the desired inequality.
 for some 
?
What if 
 and 
 are continuous?
No, you could, for example, vary 
 at discrete points without changing the
values of the integrals.  If 
 and 
 are continuous, then the assertion
is true.
In fact, suppose that for each 
, there is an 
 with 
.
Then the inequality holds true in an open neighborhood of 
 since 
 and 
 are
continuous.  So 
 since the integrand is always
non-negative and is positive on some subinterval of 
.  Expanding out
gives 
 for all
.  Since the quadratic has no solutions, it must be that its
discriminant is negative.
Let 
, 
, 
 and 
 for all 
 in 
for 
.  Then part (a) gives the inequality of Theorem 1-1 (2).
Note, however, that the equality condition does not follow from (a).
 is called
norm preserving if 
, and inner product preserving
if 
. 
 is norm preserving if and only if 
 is inner product
preserving.
If 
 is inner product preserving, then one has by Theorem 1-1 (4):
Similarly, if 
 is norm preserving, then the polarization identity together
with the linearity of T give:
.
 is 1-1, and that 
is of the same sort.
Let 
 be norm preserving.  Then 
 implies 
, i.e. the kernel
of 
 is trivial.  So T is 1-1.  Since 
 is a 1-1 linear map of a finite
dimensional vector space into itself, it follows that 
 is also onto.  In
particular, 
 has an inverse.  Further, given 
, there is a 
 with
, and so 
, since 
 is
norm preserving.  Thus 
 is norm preserving, and hence also inner
product preserving.
 and 
 in 
 are both non-zero, then the angle
between 
 and 
, denoted 
, is defined to be 
which makes sense by Theorem 1-1 (2).  The linear transformation 
 is
angle preserving if 
 is 1-1 and for 
, one has 
.
 is norm preserving, then 
 is angle preserving.
Assume 
 is norm preserving.  By Problem 1-7, 
 is inner product preserving.
So 
.
 of 
 and numbers
 such that 
, prove that 
is angle preserving if and only if all 
 are equal.
The assertion is false.  For example, if 
, 
,
, 
, and 
, then 
.  Now, 
,
but 
 showing
that T is not angle preserving.
To correct the situation, add the condition that the 
 be pairwise
orthogonal, i.e. 
 for all 
.  Using bilinearity,
this means that: 
 because
all the cross terms are zero.
Suppose all the 
 are equal in absolute value.  Then one has
 because all the 
 are equal and
cancel out.  So, this condition suffices to make 
 be angle preserving.
Now suppose that 
 for some 
 and 
 and that
.  Then 
since 
.
So, this condition suffices to make 
 not be angle preserving.
?
The angle preserving 
 are precisely those which can be expressed in the
form 
 where U is angle preserving of the kind in part (b), V is
norm preserving, and the operation is functional composition.
Clearly, any 
 of this form is angle preserving as the composition of two
angle preserving linear transformations is angle preserving.  For the
converse, suppose that 
 is angle preserving.  Let 
 be
an orthogonal basis of 
.  Define 
 to be the linear
transformation such that 
 for each 
.  
Since the 
 are pairwise orthogonal and 
 is angle preserving, the
 are also pairwise orthogonal.  In particular,
 because the cross terms
all cancel out.  This proves that 
 is norm preserving.  Now define 
 to
be the linear transformation 
.  Then clearly 
 and
 is angle preserving because it is the composition of two angle preserving
maps.  Further, 
 maps each 
 to a scalar multiple of itself; so 
is a map of the type in part (b).  This completes the characterization.
, let 
have the matrix 
.  Show that 
 is angle
preserving and that if 
, then 
.
The transformation 
 is 1-1 by Cramer's Rule because the determinant of
its matrix is 1.  Further, 
 is norm preserving since
by the Pythagorean Theorem.  By Problem 8(a), it follows that 
 is angle
preserving.
If 
, then one has 
.
Further, since 
 is norm preserving, 
.  By the definition
of angle, it follows that 
.
 is a linear transformation, show
that there is a number 
 such that 
 for 
.
Let 
 be the maximum of the absolute values of the entries in the matrix of 
 and 
.  One has 
.  (Correction: Bound should be square root of n times mN.)
 and 
, show that
 and 
.
Note that 
 and 
 denote points in 
.
This is a perfectly straightforward computation in terms of the coordinates
of 
 using only the definitions of inner product and norm.
 denote the dual space of the vector space 
.
If 
, define 
.  Define 
 by 
.  Show that 
 is a 1-1
linear transformation and conclude that every 
 is
 for a unique 
.
One needs to verify the trivial results that (a) 
 is a linear
tranformation and (b) 
.  These
follow from bilinearity; the proofs
are omitted.  Together these imply that 
 is a linear transformation.
Since 
 for 
, 
 has no non-zero vectors
in its kernel and so is 1-1.  Since the dual space has dimension n, it follows
that 
 is also onto.  This proves the last assertion.
, then 
 and 
 are called
perpendicular (or orthogonal) if 
.  If 
 and 
are perpendicular, prove that 
.
By bilinearity of the inner product, one has for perpendicular 
 and 
: