Calculus.jl

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included complex step finite method for the gradient computation

13

Hi, I've just included the "complex" step method for the multivariable finite_difference, so it can be used to compute the gradient too.

flavioluiz

code to treat differentiate as a first class higher order function

7

I added a macro @define to store the code of a function when creating it, and another one @makeDerivative to create functions that are derivatives. It becomes possible to do:...

Eskatrem

Combine terms in multiplication and addition

1

magistere

Add cancellation of common variables in 'simplify'

7

magistere

Disallow second_derivative and hessian functions to be called without g

1

Currently, `derivative.jl` has the two following definintions ``` .jl second_derivative(f::Function) = second_derivative(f, derivative(f), :scalar, :central) hessian(f::Function) = second_derivative(f, gradient(f), :vector, :central) ``` however we discussed that by estimating the first...

eriktaubeneck

Tests of differentiate() compares expressions too strictly

7

Correct changes to the implementations of differentiate() and simpelify() breaks tests if the order of arguements are changed. ``` julia >>> isequal(:(a+b) , :(b+a)) false ``` The test should use...

ivarne

Updated differentiate.jl to be make finite differences based on a symbolic expression

2

Does doing this make more sense than putting it into finite_difference and having it called by derivative()? ``` Julia julia> differentiate(:(e^x),:(x)) :(*(^(e,x),log(e))) julia> using Calculus julia> differentiate(:(e^x),:(x),1.0) 2.718281828459045 julia> differentiate(:(e^x),:(x),0.0)...

jckt

Evaluate symbolic derivatives at specific points

10

It would be useful to provide a tool like `differentiate(:(exp(x)), :x))` that returns a compiled function that is immediately evaluable at specific values of `x`.

johnmyleswhite

replace methods

1

Hi Most of mathematical languages have various `replace` methods and rules to work with expressions, e.g. see ref[1] & ref[2]. What do you think? Is it useful to add some...

DANA-Laboratory

Simplify(::Expr) failed

3

in v0.1.5 `simplify` can not handle Float64(NaN) check this code ``` julia using calculus simplify(:(1-0/0)) ``` thanks

DANA-Laboratory