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The linear dependency of a sequence of vectors does not depend of the order of the terms in the sequence. This allows defining linear independence for a finite set of vectors: A finite set of vectors is linearly independent if the sequence obtained by ordering them is linearly independent. In other words, one has the following result that is ...
In mathematics, algebras A, B over a field k inside some field extension of k are said to be linearly disjoint over k if the following equivalent conditions are met: (i) The map induced by (,) is injective.
In three-dimensional Euclidean space, these three planes represent solutions to linear equations, and their intersection represents the set of common solutions: in this case, a unique point. The blue line is the common solution to two of these equations. Linear algebra is the branch of mathematics concerning linear equations such as:
The concepts of dependence and independence of systems are partially generalized in numerical linear algebra by the condition number, which (roughly) measures how close a system of equations is to being dependent (a condition number of infinity is a dependent system, and a system of orthogonal equations is maximally independent and has a ...
Let T be a complete L-theory.An L-formula φ(x,y) is said to have the independence property (with respect to x, y) if in every model M of T there is, for each n = {0,1,...,n − 1} < ω, a family of tuples b 0,...,b n−1 such that for each of the 2 n subsets X of n there is a tuple a in M for which
Baker's Theorem — If , …, are linearly independent over the rational numbers, then for any algebraic numbers , …,, not all zero, we have | + + + | > where H is the maximum of the heights of and C is an effectively computable number depending on n, and the maximum d of the degrees of . (If β 0 is nonzero then the assumption that are linearly independent can be dropped.)
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This is an outline of topics related to linear algebra, the branch of mathematics concerning linear equations and linear maps and their representations in vector spaces and through matrices. Linear equations