Online Algebra Calculator

Many of users do not use powerful **Maxima computer algebra system** for systematic work, but for ad hoc algebraic calculations – equations, functions, matrixes, etc. only.
To do this job, it is not useful to download the whole application from a web site.

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factor (x^6+x^5+2*x^4...

F:(a^3+b*c^2)^2+(a^2+...

G:ev(F,[a:d,d:a,b:c,c...

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(%i1)describe(laplace); -- Function: laplace (<expr>, <t>, <s>) Attempts to compute the Laplace transform of <expr> with respect to the variable <t> and transform parameter <s>. `laplace' recognizes in <expr> the functions `delta', `exp', `log', `sin', `cos', `sinh', `cosh', and `erf', as well as `derivative', `integrate', `sum', and `ilt'. If laplace fails to find a transform the function `specint' is called. `specint' can find the laplace transform for expressions with special functions like the bessel functions `bessel_j', `bessel_i', ... and can handle the `unit_step' function. See also `specint'. If `specint' cannot find a solution too, a noun `laplace' is returned. <expr> may also be a linear, constant coefficient differential equation in which case `atvalue' of the dependent variable is used. The required atvalue may be supplied either before or after the transform is computed. Since the initial conditions must be specified at zero, if one has boundary conditions imposed elsewhere he can impose these on the general solution and eliminate the constants by solving the general solution for them and substituting their values back. `laplace' recognizes convolution integrals of the form `integrate (f(x) * g(t - x), x, 0, t)'; other kinds of convolutions are not recognized. Functional relations must be explicitly represented in <expr>; implicit relations, established by `depends', are not recognized. That is, if <f> depends on <x> and <y>, `f (x, y)' must appear in <expr>. See also `ilt', the inverse Laplace transform. Examples: (%i1) laplace (exp (2*t + a) * sin(t) * t, t, s); a %e (2 s - 4) (%o1) --------------- 2 2 (s - 4 s + 5) (%i2) laplace ('diff (f (x), x), x, s); (%o2) s laplace(f(x), x, s) - f(0) (%i3) diff (diff (delta (t), t), t); 2 d (%o3) --- (delta(t)) 2 dt (%i4) laplace (%, t, s); ! d ! 2 (%o4) - -- (delta(t))! + s - delta(0) s dt ! !t = 0 (%i5) assume(a>0)$ (%i6) laplace(gamma_incomplete(a,t),t,s),gamma_expand:true; - a - 1 gamma(a) gamma(a) s (%o6) -------- - ----------------- s 1 a (- + 1) s (%i7) factor(laplace(gamma_incomplete(1/2,t),t,s)); s + 1 sqrt(%pi) (sqrt(s) sqrt(-----) - 1) s (%o7) ----------------------------------- 3/2 s + 1 s sqrt(-----) s (%i8) assume(exp(%pi*s)>1)$ (%i9) laplace(sum((-1)^n*unit_step(t-n*%pi)*sin(t),n,0,inf),t,s),simpsum; %i %i ------------------------ - ------------------------ - %pi s - %pi s (s + %i) (1 - %e ) (s - %i) (1 - %e ) (%o9) --------------------------------------------------- 2 (%i9) factor(%); %pi s %e (%o9) ------------------------------- %pi s (s - %i) (s + %i) (%e - 1) There are also some inexact matches for `laplace'. Try `?? laplace' to see them. (%o1) true (%i2)

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describe(eigenvalues); -- Function: eigenvalues (<M>) -- Function: eivals (<M>) Returns a list of two lists containing the eigenvalues of the matrix <M>. The first sublist of the return value is the list of eigenvalues of the matrix, and the second sublist is the list of the multiplicities of the eigenvalues in the corresponding order. `eivals' is a synonym for `eigenvalues'. `eigenvalues' calls the function `solve' to find the roots of the characteristic polynomial of the matrix. Sometimes `solve' may not be able to find the roots of the polynomial; in that case some other functions in this package (except `innerproduct', `unitvector', `columnvector' and `gramschmidt') will not work. In some cases the eigenvalues found by `solve' may be complicated expressions. (This may happen when `solve' returns a not-so-obviously real expression for an eigenvalue which is known to be real.) It may be possible to simplify the eigenvalues using some other functions. The package `eigen.mac' is loaded automatically when `eigenvalues' or `eigenvectors' is referenced. If `eigen.mac' is not already loaded, `load ("eigen")' loads it. After loading, all functions and variables in the package are available. There are also some inexact matches for `eigenvalues'. Try `?? eigenvalues' to see them. (%o1) true (%i2)

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describe(eigenvectors); -- Function: eigenvectors (<M>) -- Function: eivects (<M>) Computes eigenvectors of the matrix <M>. The return value is a list of two elements. The first is a list of the eigenvalues of <M> and a list of the multiplicities of the eigenvalues. The second is a list of lists of eigenvectors. There is one list of eigenvectors for each eigenvalue. There may be one or more eigenvectors in each list. `eivects' is a synonym for `eigenvectors'. The package `eigen.mac' is loaded automatically when `eigenvalues' or `eigenvectors' is referenced. If `eigen.mac' is not already loaded, `load ("eigen")' loads it. After loading, all functions and variables in the package are available. The flags that affect this function are: `nondiagonalizable' is set to `true' or `false' depending on whether the matrix is nondiagonalizable or diagonalizable after `eigenvectors' returns. `hermitianmatrix' when `true', causes the degenerate eigenvectors of the Hermitian matrix to be orthogonalized using the Gram-Schmidt algorithm. `knowneigvals' when `true' causes the `eigen' package to assume the eigenvalues of the matrix are known to the user and stored under the global name `listeigvals'. `listeigvals' should be set to a list similar to the output `eigenvalues'. The function `algsys' is used here to solve for the eigenvectors. Sometimes if the eigenvalues are messy, `algsys' may not be able to find a solution. In some cases, it may be possible to simplify the eigenvalues by first finding them using `eigenvalues' command and then using other functions to reduce them to something simpler. Following simplification, `eigenvectors' can be called again with the `knowneigvals' flag set to `true'. See also `eigenvalues'. Examples: A matrix which has just one eigenvector per eigenvalue. (%i1) M1 : matrix ([11, -1], [1, 7]); [ 11 - 1 ] (%o1) [ ] [ 1 7 ] (%i2) [vals, vecs] : eigenvectors (M1); (%o2) [[[9 - sqrt(3), sqrt(3) + 9], [1, 1]], [[[1, sqrt(3) + 2]], [[1, 2 - sqrt(3)]]]] (%i3) for i thru length (vals[1]) do disp (val[i] = vals[1][i], mult[i] = vals[2][i], vec[i] = vecs[i]); val = 9 - sqrt(3) 1 mult = 1 1 vec = [[1, sqrt(3) + 2]] 1 val = sqrt(3) + 9 2 mult = 1 2 vec = [[1, 2 - sqrt(3)]] 2 (%o3) done A matrix which has two eigenvectors for one eigenvalue (namely 2). (%i1) M1 : matrix ([0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 2]); [ 0 1 0 0 ] [ ] [ 0 0 0 0 ] (%o1) [ ] [ 0 0 2 0 ] [ ] [ 0 0 0 2 ] (%i2) [vals, vecs] : eigenvectors (M1); (%o2) [[[0, 2], [2, 2]], [[[1, 0, 0, 0]], [[0, 0, 1, 0], [0, 0, 0, 1]]]] (%i3) for i thru length (vals[1]) do disp (val[i] = vals[1][i], mult[i] = vals[2][i], vec[i] = vecs[i]); val = 0 1 mult = 2 1 vec = [[1, 0, 0, 0]] 1 val = 2 2 mult = 2 2 vec = [[0, 0, 1, 0], [0, 0, 0, 1]] 2 (%o3) done There are also some inexact matches for `eigenvectors'. Try `?? eigenvectors' to see them. (%o1) true (%i2)