名字
发表于 2025-3-25 05:04:44
An Introduction to the Theory of Determinants,In this chapter, we will introduce and study a remarkable function called the determinant, which assigns to an . matrix . over a field . a scalar . having two remarkable properties: . if and only if . is invertible, and if . is also in ., then .. The latter property is referred to as the product formula.
诱导
发表于 2025-3-25 10:26:34
Vector Spaces,A vector space is a set . whose elements, called vectors, can be added and subtracted: in fact, a vector space is an abelian group under addition.
坦白
发表于 2025-3-25 12:03:14
Linear Mappings,The purpose of this chapter is to introduce linear mappings.
myopia
发表于 2025-3-25 18:44:56
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exhilaration
发表于 2025-3-25 22:39:01
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埋葬
发表于 2025-3-26 02:17:57
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filial
发表于 2025-3-26 08:08:56
Conference proceedings 20091st edition. and its minimal polynomial has only simple roots. It would be useful to have a result that would allow one to predict that . is semisimple on the basis of a criterion that is simpler than finding the minimal polynomial, which, after all, requires knowing the roots of the characteristic polynomial.
characteristic
发表于 2025-3-26 10:22:34
Some Promising Trends in Ice Mechanics,rary linear mapping . having the property that all the roots of its characteristic polynomial lie in .. To describe this situation, let us say that . contains the eigenvalues of .. Recall that a linear mapping . is also called an endomorphism of ., and in this chapter, we will usually use that term.
MAOIS
发表于 2025-3-26 12:50:38
Unitary Diagonalization and Quadratic Forms,. and its minimal polynomial has only simple roots. It would be useful to have a result that would allow one to predict that . is semisimple on the basis of a criterion that is simpler than finding the minimal polynomial, which, after all, requires knowing the roots of the characteristic polynomial.
珐琅
发表于 2025-3-26 17:40:01
The Structure Theory of Linear Mappings,rary linear mapping . having the property that all the roots of its characteristic polynomial lie in .. To describe this situation, let us say that . contains the eigenvalues of .. Recall that a linear mapping . is also called an endomorphism of ., and in this chapter, we will usually use that term.