Linear algebra : methods & tips guides and supports students with differing levels of preparation and experience in mathematics. In this application you will find methods and tips very concentrated in Linear Algebra.
If you are looking for a very practical, very useful short which summarizes the methods of solving all linear algebra problems then you are in the right corner.
The way to solve and overcome the various difficulties of linear algebra and in a short and efficient and very clear way
linear algebra : methods & tips helps you to overcome the difficulty of different linear algebra question step by step.
Easy math? Yes, with linear algebra : methods & tips app, solving any type of linear algebra problem becomes a very fun game. Ideas are presented as clearly as possible, and you will find in this app algebra problem solving algorithms, tips, knowledge and know-how in mathematics.
The following content are obviously present in our application :
● Determinants
● Linear systems
▫ Solve a system using the Gaussian pivot method.
▫ Solve a system using Cramer's method.
● Vector spaces
▫ Determine if a set E is an R-vector space or not.
▫ Determine if a set E is a vector subspace or not.
▫ Study the freedom of a family of vectors.
▫ Generating families.
▫ Determine the rank of a family of vectors.
▫ Determine a basis of a set E.
▫ Determine a basis of the set E∩F.
● Matrix calculation
▫ Determine if a matrix A is invertible or not and calculate its inverse.
▫ Determine the rank of a matrix.
● Linear maps
▫ Show that an application is linear or not.
▫ Determine the kernel of a linear map.
▫ Determine the image of a linear map.
▫ Determine a basis of the kernel of a linear map.
▫ Determine a basis for the image of a linear map.
▫ Determine if a linear map is endomorphism, isomorphism or automorphism.
▫ Study the injectivity, surjectivity and bijectivity of a linear map.
▫ Determine the rank of a linear map.
● Diagonalization
▫ Determine the characteristic polynomial of a matrix.
▫ Diagonalize a matrix.
▫ Determine the eigen sub-spaces, the eigenvectors and a basis of an eigen sub-space.