Show non-degenerate form of subspaces sum

In summary: E_2. Then, for any w \in (E_1 + E_2), we have d(v,w) = d(v_1 + v_2, w) = d(v_1,w) + d(v_2,w) = 0. Similarly, we have d(w,v) = d(w,v_1 + v_2) = d(w,v_1) + d(w,v_2) = 0. This means that both d(v_1,w) and d(v_2,w) must be equal to zero, and the same goes for d(w,v_1) and d(w,v_2). This implies that v_1 = v_2 = 0, which means
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ktatar156
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Let [tex](E, d)[/tex] be nonzero bilinear space over [tex]K[/tex] and place conditions:
[tex]d(x,y) = d(y,x) \\
d(x,y) = - d(y,x)[/tex]
for every [tex]x,y \in E[/tex]. Show that:
if [tex]E_1[/tex] and [tex]E_2[/tex] are singular (degenerate?) bilinear subspaces relative with [tex]d[/tex] ( [tex](E_1,d|(E_1 \times E_1)[/tex] and [tex](E_2,d|(E_2 \times E_2)[/tex] are singular (degenerate?) bilinear spaces) with the same finite dimension and [tex]E_1 \cap E^d_2 = \{ \theta \}[/tex], then cutting the functional [tex]d[/tex] to the space [tex](E_1 + E_2) \times (E_1 + E_2)[/tex] is non-degenerate form.

Can anyone can help me with that?
I know that space [tex]E[/tex] is symmetric and skew-symmetric, right? And I need to show that [tex]rad(E_1+E_2) = 0[/tex] or [tex](E_1+E_2) \cap (E_1 + E_2)^d = \{ \theta \} [/tex], yes?
 
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Yes, you are correct. To prove that cutting the functional d to the space (E_1 + E_2) \times (E_1 + E_2) is non-degenerate, we need to show that the radical of (E_1 + E_2) is equal to zero or that the intersection of (E_1 + E_2) and (E_1 + E_2)^d is only the zero vector.

First, let's prove that the radical of (E_1 + E_2) is equal to zero. Assume that there exists a nonzero vector v \in (E_1 + E_2) such that d(v,w) = 0 for all w \in (E_1 + E_2). Since v \in (E_1 + E_2), we can write v = v_1 + v_2 where v_1 \in E_1 and v_2 \in E_2. Then, for any w \in (E_1 + E_2), we have d(v,w) = d(v_1 + v_2, w) = d(v_1,w) + d(v_2,w) = 0. This means that both d(v_1,w) and d(v_2,w) must be equal to zero. Since v_1 \in E_1 and v_2 \in E_2, this implies that v_1 = 0 and v_2 = 0. Therefore, v = v_1 + v_2 = 0, which contradicts our assumption that v is nonzero. Hence, the radical of (E_1 + E_2) is equal to zero.

Next, we need to show that the intersection of (E_1 + E_2) and (E_1 + E_2)^d is only the zero vector. Assume that there exists a nonzero vector v \in (E_1 + E_2) \cap (E_1 + E_2)^d. Then, for any w \in (E_1 + E_2), we have d(v,w) = 0 and d(w,v) = 0. Since v \in (E_1 + E_2), we can write v = v_1 + v_2 where v_1 \in E_1 and v_2 \in
 

FAQ: Show non-degenerate form of subspaces sum

What is a non-degenerate form of subspaces sum?

A non-degenerate form of subspaces sum is a mathematical operation where two or more subspaces are combined to form a new subspace that includes all the elements from each of the original subspaces. This new subspace is said to be non-degenerate if it does not contain any redundant or unnecessary elements.

How is a non-degenerate form of subspaces sum calculated?

The calculation of a non-degenerate form of subspaces sum involves finding the intersection of the subspaces and then combining the elements from each subspace to form a new subspace. This can be done using various mathematical techniques, such as linear algebra or set theory.

What are the benefits of using a non-degenerate form of subspaces sum?

Using a non-degenerate form of subspaces sum can help simplify complex mathematical equations and make them easier to solve. It can also help identify redundant or unnecessary elements in a set of subspaces, which can improve the efficiency and accuracy of calculations.

Can a non-degenerate form of subspaces sum be applied to any type of subspaces?

Yes, a non-degenerate form of subspaces sum can be applied to any type of subspaces, including vector spaces, affine spaces, and linear subspaces. However, the method of calculation may vary depending on the type of subspaces being used.

Are there any limitations to using a non-degenerate form of subspaces sum?

While a non-degenerate form of subspaces sum can be a useful mathematical tool, it may not always be applicable or necessary in every situation. In some cases, simpler methods may be used to achieve the same result. Additionally, the calculation may become more complex when dealing with a large number of subspaces.

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