Bug fixes, integration of solve_interms_ofvar and some changes

[n0rbed]

New stuff:

julia> symbolic_solve(1/x, x)
1-element Vector{Float64}:
 Inf

julia> symbolic_solve(x^1.5, x)
1-element Vector{BigFloat}:
 0.0

"Strict" kwarg for solve_univar @AayushSabharwal :

julia> Symbolics.solve_univar(x^1.5 + x, x)
ERROR: AssertionError: Not a polynomial

julia> Symbolics.solve_univar(x^1.5 + x, x, strict=false)
1-element Vector{SymbolicUtils.BasicSymbolic{Number}}:
roots_of(x + x^1.5, x)

Documentation and integration of solve_interms_ofvar was also added. ia_rules.jl was moved to special_cases.jl because thats more representative i think, since the solve_interms_ofvar case isnt "isolation and attraction" - its just some algebraic manipulation method we use in a special case @ChrisRackauckas (let me know what you think).

[n0rbed]

A next todo would be using ia_solve recursively instead of symbolic_solve in solve_interms_ofvar to make it more flexible

[n0rbed]

If im not mistaken, this fixes #1356

[codecov-commenter]

⚠️ Please install the to ensure uploads and comments are reliably processed by Codecov.

Codecov Report

Attention: Patch coverage is 91.89189% with 3 lines in your changes missing coverage. Please review.

Project coverage is 79.07%. Comparing base (5af597a) to head (0368c3e).
Report is 13 commits behind head on master.

Files with missing lines	Patch %	Lines
src/solver/main.jl	77.77%	2 Missing ⚠️
src/solver/postprocess.jl	87.50%	1 Missing ⚠️

❗ Your organization needs to install the Codecov GitHub app to enable full functionality.

Additional details and impacted files

@@             Coverage Diff             @@
##           master    #1357       +/-   ##
===========================================
+ Coverage    3.98%   79.07%   +75.09%     
===========================================
  Files          50       50               
  Lines        4771     4880      +109     
===========================================
+ Hits          190     3859     +3669     
+ Misses       4581     1021     -3560     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[sumiya11]

One data point:

In Maple 2024 solve(1/x = 0); returns nothing and solve(x^1.5 = 0); returns 0.0.

[n0rbed]

In Maple 2024 solve(1/x = 0); returns nothing

@sumiya11 Do you think this is preferable? i have a feeling that "nothing" could be confused with "we cant solve this".

[AayushSabharwal]

In julia 1/0 is Inf and 1/Inf is 0. I feel like Inf is the better return value here.

[sumiya11]

Should solve_univar be renamed to solve_univar_poly ?

[n0rbed]

solve_multivar should be renamed to solve_multivar_poly aswell then - i'll do it when i get the time

[sumiya11]

In Maple 2024 solve(1/x = 0); returns nothing

@sumiya11 Do you think this is preferable? i have a feeling that "nothing" could be confused with "we cant solve this".

I guess Inf is fine. My thoughts are:

Inf for 1/x is fine since substituting Inf back gives zero. In that vein, solve(x / x^2) should return nothing. So it is not clear to me how useful Inf could be in general.

On the other hand, returning nothing for 1/x is easy and correct (in a sense that the roots of P(x) / Q(x) = 0 is a subset of the roots of P(x) = 0).

[sumiya11]

Another data point:

>>> sympy.solve(1/x)
[]

>>> sympy.solve(x**(1.5))
[0.0]

UPD: Reduce

julia> "solve(1/x, x)" |> rcall
"{}"

julia> "solve(x^(1.5), x)" |> rcall
"{x=0,x=0}"

[n0rbed]

@sumiya11 Looks like its universal
Mathematica 14.0

In[1] := Solve[1/x == 0]
Out[1]= {}

Matlab

>> syms x
>> solve(1/x == 0, x)
 
ans =
 
Empty sym: 0-by-1

[AayushSabharwal]

If this renaming is happening and may happen in the future, it would be good to either document the API as experimental (so users know it can undergo breaking changes) and/or make the old names fall back to the new ones.

EDIT: Meant to reply to #1357 (comment)

[n0rbed]

Yes i'll deprecate them and make them call the new renamed versions

[n0rbed]

Does the output of 1/x matter in MTK btw? which would u prefer @AayushSabharwal

[AayushSabharwal]

It doesn't particularly matter for MTK