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Sylvester's law of inertia: every real symmetric form is congruent to , and is unique
Statement
Every symmetric bilinear form on a finite-dimensional real vector space is congruent to exactly one normal form
Equivalently, the numbers of positive, negative, and zero diagonal entries are independent of the diagonalizing basis.
Facts & Assumptions
Given: A symmetric bilinear form on a finite-dimensional real vector space .
Every real symmetric matrix is congruent to a diagonal matrix (Over a field of characteristic not , every symmetric matrix is congruent to a diagonal matrix).
Positive and negative definiteness and the inertia data have the meanings stated for real symmetric forms (Positive and negative definiteness, the inertia , rank , and signature of a real symmetric bilinear or quadratic form).
The constructed real field has the least-upper-bound property and hence is complete ordered (The Cauchy-sequence reals have the least-upper-bound property), so every positive real has a nonzero positive square root (Square roots exist: a unique with ; the positives are ).
For finite-dimensional subspaces , (The dimension formula: for finite-dimensional linear subspaces and of , the subspaces and are finite-dimensional and ).
A subspace of a finite-dimensional -dimensional space has dimension at most (If and is a linear subspace of , then is finite-dimensional, , and if and only if , clause 1).
Rank-nullity gives the dimension of a kernel as ambient dimension minus rank (Rank-nullity: ).
Proof
By [L1], choose a basis in which the matrix is diagonal, say with positive entries , negative entries , and zero entries. For each nonzero , [L3] supplies ; replacing the corresponding basis vector by times it changes to or . This proves existence of the displayed normal form, including and the empty basis when .
In this normal form, the positive coordinate subspace has dimension and the form is positive definite on it. Let be the span of the negative and zero coordinates, of dimension . If a positive-definite subspace had , [L4] and [L5] applied to would give . A nonzero vector there has form value at most , a contradiction. Thus is the intrinsic maximum dimension of a positive-definite subspace.
Applying step 2.1 to shows that is the intrinsic maximum dimension of a negative-definite subspace. The radical is the kernel of the associated map; in normal form its rank is , so [L6] gives its dimension .
Any congruent normal form represents the same bilinear form and therefore has the same two intrinsic maxima and radical dimension. Hence its triple is the same , proving uniqueness.
Steps 1.1 and 4.1 prove existence and uniqueness for all finite dimensions and for degenerate as well as nondegenerate forms.
Depends on
- Over a field of characteristic not $2$, every symmetric matrix is congruent to a diagonal matrix
- Positive and negative definiteness, the inertia $(p,q,r)$, rank $p+q$, and signature $p-q$ of a real symmetric bilinear or quadratic form
- The Cauchy-sequence reals have the least-upper-bound property
- Square roots exist: a unique $\sqrt{a} \ge 0$ with $(\sqrt{a})^2 = a$; the positives are $\{x^2 : x \neq 0\}$
- The dimension formula: for finite-dimensional linear subspaces $U$ and $W$ of $V$, the subspaces $U + W$ and $U \cap W$ are finite-dimensional and $\dim_F(U+W) + \dim_F(U \cap W) = \dim_F U + \dim_F W$
- If $\dim_F V = n$ and $U$ is a linear subspace of $V$, then $U$ is finite-dimensional, $\dim_F U \le n$, and $\dim_F U = n$ if and only if $U = V$
- Rank-nullity: $\dim_F V=\operatorname{nullity}T+\operatorname{rank}T$
Used by
- Two real symmetric bilinear forms are congruent if and only if they have the same inertia Corollary
- q(x,y)=2x²+4xy+5y²=2(x+y)²+3y² has inertia (2,0,0) Example
- Sylvester's criterion: a real symmetric n× n matrix with n≥1 is positive definite if and only if all leading principal minors are positive Theorem
Cited to discharge well-definedness by Positive and negative definiteness, the inertia (p,q,r), rank p+q, and signature p-q of a real symmetric bilinear or quadratic form.
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Sources
- H. Pinkham, Linear Algebra, §7.7 (standard reference, not scraped)