It's not always the case that symmetric matrices commute so actually
checking if it is symmetric is not sufficient e.g.:

In [83]: M = Matrix([[2*x**2, x], [x, x**2]])

In [84]: M.is_symmetric()
Out[84]: True

In [85]: M*M.diff(x) == M.diff(x)*M
Out[85]: False

Maybe there is something that can be said more generally about
`exp(M(t)).diff(t)` when `M` is symmetric but does not necessarily
commute with `M.diff(t)`...


On Thu, 19 Mar 2020 at 18:34, Milan Jolly <[email protected]> wrote:
>
> In ODE systems roadmap, you have mentioned that for system of ODEs where the 
> coefficient matrix is non-constant, if the coefficient matrix A(t) is 
> symmetric, then A(t) and its anti derivative B(t) commute and thus we get the 
> solution based on this fact. But it is also mentioned that if A and B 
> commuting is more general than when A is symmetric, that is, it is possible 
> that A is not symmetric but A and B commute. So, for that solver, should we 
> first compute its anti derivative and test it that commutes with A or just 
> check if A is symmetric and use the solution?
>
> On Wednesday, March 18, 2020 at 3:18:31 AM UTC+5:30, Oscar wrote:
>>
>> That sounds reasonable.
>>
>> Note that we can't start raising NotImplementedError yet. You will
>> need to think about how to introduce the new code gradually while
>> still ensuring that dsolve falls back on the old code for cases not
>> yet handled by the new code.
>>
>> On Tue, 17 Mar 2020 at 17:51, Milan Jolly <[email protected]> wrote:
>> >
>> > So, I have made a rough layout of the main function that will be used to 
>> > solve ODEs with the methods like 
>> > neq_nth_order_linear_constant_coeff_homogeneous/nonhomogeneous, 
>> > neq_nth_linear_symmetric_coeff_homogeneous/nonhomogeneous, special case 
>> > non-linear solvers, etc.
>> >
>> > Some notations used:
>> >    eqs: Equations, funcs: dependent variables, t: independent variable, 
>> > wcc: weakly connected component, scc: strongly connected component
>> >
>> > Introduction to helper functions that will be used(these are temporary 
>> > names, parameters and return elements and may be changed if required):
>> >
>> > 1. match_ode:-
>> >         Parameters: eqs, funcs, t
>> >         Returns: dictionary which has important keys like: order(a dict 
>> > that has func as a key and maximum order found as value), is_linear, 
>> > is_constant, is_homogeneous, eqs, funcs.
>> >
>> > 2. component_division:-
>> >         Paramters: eqs, funcs
>> >         Returns: A 3D list where the eqs are first divided into its wccs 
>> > and then into its sccs.
>> >         This function is suggested to be implemented later. So, until all 
>> > the other solvers are not ready(tested and working), this function will 
>> > just take eqs and return [[eqs]].
>> >
>> > 3. get_coeff_matrix:-
>> >         Parameters: eqs, funcs
>> >         Returns: coefficient matrix A(t) and f(t)
>> >         This function takes in a first order linear ODE and returns matrix 
>> > A(t) and f(t) from X' = A(t) * X + f(t).
>> >
>> > 4. nth_order_to_first_order:-
>> >         Parameters: eqs, order
>> >         Returns: first order ODE with new introduced dependent variables.
>> >
>> > And all the first order linear solvers mentioned above.
>> >
>> > Now, besides the main function, there are two separate functions depending 
>> > on whether the system of ODEs is linear or not, namely _linear_ode_sol and 
>> > _non_linear_ode_sol.
>> >
>> > 1. _first_order_linear_ode_sol:-
>> >         Parameters: match dict(obtained earlier and maybe modified in 
>> > ode_sol)
>> >         Returns: Dict with keys as func and value as its solution that 
>> > solves the ODE.
>> >         Working: First, extracts A(t) and f(t) using get_coeff_matrix, 
>> > then using match dict, identify which solver is required and solve the ODE 
>> > if it is possible to do so. For example: Case where A(t) is not symmetric 
>> > isn't solved.
>> >
>> > 2. _non_linear_ode_sol has similar Parameters and Returns but the function 
>> > operates differently that's why it is essential to use a different 
>> > function. But I don't have a clear understanding
>> >    of how to design _non_linear_ode_sol yet but here is what I have came 
>> > up with: First match the condition where it is possible seperate out the 
>> > independent variable to get a relationship
>> >    between the dependent variables and then finally, just use the special 
>> > solver to solve the ODE.
>> >
>> > Now, coming to the main function ode_sol(for now, I haven't considered 
>> > initial values):-
>> >   Parameters: eqs, funcs, t
>> >   Returns: Solution in a dict form where func is the key and value is the 
>> > solution for that corresponding func.
>> >
>> >   Working:
>> >   The steps of its working-
>> >       1. Preprocess the equations.
>> >       2. Get the match dict using match_ode function.
>> >       3. Convert nth order equations to first order equations using 
>> > nth_order_to_first_order while storing the funcs seperately so that we can 
>> > later filter out the dependent variables that were introduced in this step.
>> >       4. Get the 3D list of equations using component_division function.
>> >       5. Iterate through the wccs and solve and store solutions seperately 
>> > but for sccs, first solve the first set of equations in a scc, then 
>> > substitute the solutions found in the first set of the current scc to the 
>> > second
>> >          set of current scc. Keep doing this until the all the sets for a 
>> > particular scc is solved.
>> >       6. For solving a component, choose either _linear_ode_sol or 
>> > _non_linear_ode_sol depending upon the set of equations to be solved.
>> >       7. Return a dict by taking out values from the solution obtained 
>> > using all the dependent variables in funcs as there may be more variables 
>> > introduced when we made the system into first order.
>> >
>> > For now, this is what I have came up with. Obviously the order in which we 
>> > will proceed is, build the basic layout of the main function and 
>> > component_division will just increase the number of dimensions to 3 
>> > rudimentarily as we
>> > will have to first ensure that the general solvers work well since working 
>> > on both of them simultaneously will make it tough to pinpoint the errors. 
>> > Along with that, non-linear solvers can be implemented later, we can just 
>> > raise a
>> > NotImplementedError for now till we have completed both the general linear 
>> > solvers and the component_division and then add the special case solvers.
>> >
>> > On Tuesday, March 17, 2020 at 3:02:29 AM UTC+5:30, Oscar wrote:
>> >>
>> >> There are possibilities to go from nonlinear to linear e.g.:
>> >>
>> >> In [6]: x, y = symbols('x, y', cls=Function)
>> >>
>> >> In [7]: eqs = [x(t).diff(t)**2 - y(t)**2, y(t).diff(t)**2 - x(t)**2]
>> >>
>> >> In [8]: eqs
>> >> Out[8]:
>> >> ⎡                    2                      2⎤
>> >> ⎢   2      ⎛d       ⎞      2      ⎛d       ⎞ ⎥
>> >> ⎢- y (t) + ⎜──(x(t))⎟ , - x (t) + ⎜──(y(t))⎟ ⎥
>> >> ⎣          ⎝dt      ⎠             ⎝dt      ⎠ ⎦
>> >>
>> >> In [9]: solve(eqs, [x(t).diff(t), y(t).diff(t)], dict=True)
>> >> Out[9]:
>> >> ⎡⎧d                d              ⎫  ⎧d                d             ⎫
>> >>  ⎧d               d              ⎫  ⎧d               d             ⎫⎤
>> >> ⎢⎨──(x(t)): -y(t), ──(y(t)): -x(t)⎬, ⎨──(x(t)): -y(t), ──(y(t)):
>> >> x(t)⎬, ⎨──(x(t)): y(t), ──(y(t)): -x(t)⎬, ⎨──(x(t)): y(t), ──(y(t)):
>> >> x(t)⎬⎥
>> >> ⎣⎩dt               dt             ⎭  ⎩dt               dt            ⎭
>> >>  ⎩dt              dt             ⎭  ⎩dt              dt            ⎭⎦
>> >>
>> >> On Mon, 16 Mar 2020 at 15:48, Milan Jolly <[email protected]> wrote:
>> >> >
>> >> > Thanks for the suggestion, I have started with the design for these 
>> >> > solvers. But I have one doubt, namely since now we are using 
>> >> > linear_eq_to_matrix function to check if the system of ODEs is linear 
>> >> > or not, would we require the canonical rearrangements part? Or rather 
>> >> > are there other cases when we can reduce non-linear ODEs into linear 
>> >> > ODEs.
>> >> >
>> >> > On Monday, March 16, 2020 at 2:53:57 AM UTC+5:30, Oscar wrote:
>> >> >>
>> >> >> That seems reasonable to me. Since the plan is a total rewrite I think
>> >> >> that it would be good to put some time in at the beginning for
>> >> >> designing how all of these pieces would fit together. For example even
>> >> >> if the connected components part comes at the end it would be good to
>> >> >> think about how that code would fit in from the beginning and to
>> >> >> clearly document it both in issues and in the code.
>> >> >>
>> >> >> Getting a good design is actually more important than implementing all
>> >> >> of the pieces. If the groundwork is done then other contributors in
>> >> >> future can easily implement the remaining features one by one. Right
>> >> >> now it is not easy to improve the code for systems because of the way
>> >> >> that it is structured.
>> >> >>
>> >> >> On Sun, 15 Mar 2020 at 19:27, Milan Jolly <[email protected]> wrote:
>> >> >> >
>> >> >> > Thanks for your reply. I have planned a rough layout for the phases. 
>> >> >> > I took a lot of time this past month to understand all the 
>> >> >> > mathematics that will be involved and have grasped some part of it.
>> >> >> >
>> >> >> > If I am lucky and get selected for GSOC'20 for this organisation, 
>> >> >> > then the below is the rough plan. Please comment on suggestions if 
>> >> >> > necessary.
>> >> >> >
>> >> >> > Community Bonding phase:
>> >> >> > 1. Using matrix exponential to solve first order linear constant 
>> >> >> > coefficient homogeneous systems(n equations).
>> >> >> > 2. Adding new test cases and/or updating old ones.
>> >> >> > 3. Removing and closing related issues if they are solved by the 
>> >> >> > addition of this general solver. Identifying and removing the 
>> >> >> > special cases solvers which are covered by this general solver.
>> >> >> >
>> >> >> > Phase I:
>> >> >> > 1. Adding technique to solve first order constant coefficient 
>> >> >> > non-homogeneous systems(n equations).
>> >> >> > 2. Adding the functionality that reduces higher order linear ODEs to 
>> >> >> > first order linear ODEs(if not done already, and if done, then 
>> >> >> > incorporating it to solve higher order ODEs).
>> >> >> > 3. Adding a special case solver when non-constant linear first order 
>> >> >> > ODE has symmetric coefficient matrix.
>> >> >> >
>> >> >> > Phase II:
>> >> >> > 1. Adding technique to solve non-constant non-homogeneous linear ODE 
>> >> >> > based off the solver added by the end of Phase I.
>> >> >> > 2. Evaluating and eliminating unnecessary solvers.
>> >> >> > 3. Closing related issues solved by the general solvers and 
>> >> >> > identifying and removing unwanted solvers.
>> >> >> > 4. Adding basic rearrangements to simplify the system of ODEs.
>> >> >> >
>> >> >> > Phase III:
>> >> >> > 1. Dividing the ODEs by evaluating which sub-systems are weakly and 
>> >> >> > strongly connected and handling both of these cases accordingly.
>> >> >> > 2. Adding a special case solver where the independent variable can 
>> >> >> > be eliminated and thus solving the system becomes easier.
>> >> >> > 3. Wrapping things up: adding test cases, eliminating unwanted 
>> >> >> > solvers and updating documentation.
>> >> >> >
>> >> >> > This is the rough layout and my plan for summer if I get selected. 
>> >> >> > If this plan seems ok then I would include this plan in my proposal.
>> >> >> >
>> >> >> > On Saturday, March 14, 2020 at 9:37:31 PM UTC+5:30, Oscar wrote:
>> >> >> >>
>> >> >> >> It's hard to say how much time each of these would take. The roadmap
>> >> >> >> aims to completely replace all of the existing code for systems of
>> >> >> >> ODEs. How much of that you think you would be able to do is up to 
>> >> >> >> you
>> >> >> >> if making a proposal.
>> >> >> >>
>> >> >> >> None of the other things described in the roadmap is implemented
>> >> >> >> anywhere as far as I know. Following the roadmap it should be 
>> >> >> >> possible
>> >> >> >> to close all of these issues I think:
>> >> >> >> https://github.com/sympy/sympy/issues?q=is%3Aopen+is%3Aissue+label%3Asolvers.dsolve.system
>> >> >> >>
>> >> >> >> On Fri, 13 Mar 2020 at 22:30, Milan Jolly <[email protected]> 
>> >> >> >> wrote:
>> >> >> >> >
>> >> >> >> > I have  mostly read and understood matrix exponentials and Jordan 
>> >> >> >> > forms along with the ODE systems roadmap. But I am unclear as to 
>> >> >> >> > what has already been done when it comes to implementing the 
>> >> >> >> > general solvers. For example: The matrix exponentials part has 
>> >> >> >> > already been implemented and now I have a PR that has revived the 
>> >> >> >> > matrix exponential code.
>> >> >> >> >
>> >> >> >> > I want to make a proposal and contribute to make these general 
>> >> >> >> > solvers during this summer if my proposal gets accepted. But I am 
>> >> >> >> > unclear what should be the parts I need to work during community 
>> >> >> >> > bonding period, phase 1, phase 2 and phase 3 as I am unaware how 
>> >> >> >> > much time each part of the general solvers would take.
>> >> >> >> >
>> >> >> >> > If someone can help me in this regard(helping me with these 2 
>> >> >> >> > questions) then it would be great.
>> >> >> >> >
>> >> >> >> >
>> >> >> >> > On Tue, Feb 25, 2020, 5:09 AM Milan Jolly <[email protected]> 
>> >> >> >> > wrote:
>> >> >> >> >>
>> >> >> >> >> I will go through the roadmap. Also, I will work on reviving and 
>> >> >> >> >> finishing the stalled PRs namely the matrix exponential one for 
>> >> >> >> >> now as I am interested in working towards this. Thanks.
>> >> >> >> >>
>> >> >> >> >> On Mon, Feb 24, 2020, 9:56 PM Oscar Benjamin 
>> >> >> >> >> <[email protected]> wrote:
>> >> >> >> >>>
>> >> >> >> >>> This section in the roadmap refers to existing stalled PRs 
>> >> >> >> >>> trying to
>> >> >> >> >>> fix the n-equations solver for constant coefficient homogeneous 
>> >> >> >> >>> ODEs
>> >> >> >> >>> which is the first step:
>> >> >> >> >>> https://github.com/sympy/sympy/wiki/ODE-Systems-roadmap#constant-coefficients---current-status
>> >> >> >> >>>
>> >> >> >> >>> A first step would be to attempt to revive one or both of those 
>> >> >> >> >>> PRs
>> >> >> >> >>> and finish them off.
>> >> >> >> >>>
>> >> >> >> >>> On Mon, 24 Feb 2020 at 05:59, Milan Jolly <[email protected]> 
>> >> >> >> >>> wrote:
>> >> >> >> >>> >
>> >> >> >> >>> > So, I am interested in rewriting parts of the current ODE as 
>> >> >> >> >>> > discussed in the roadmap. Is there any work started in that 
>> >> >> >> >>> > direction and if not then can I create a PR for the same?
>> >> >> >> >>> >
>> >> >> >> >>> > On Mon, Feb 24, 2020, 2:52 AM Oscar Benjamin 
>> >> >> >> >>> > <[email protected]> wrote:
>> >> >> >> >>> >>
>> >> >> >> >>> >> The current refactoring effort applies only to the case of 
>> >> >> >> >>> >> solving
>> >> >> >> >>> >> *single* ODEs. The ODE systems code also needs to be 
>> >> >> >> >>> >> refactored but
>> >> >> >> >>> >> (in my opinion) needs a complete rewrite. That is what the 
>> >> >> >> >>> >> roadmap is
>> >> >> >> >>> >> about (it describes how to rewrite everything). The code for 
>> >> >> >> >>> >> systems
>> >> >> >> >>> >> of ODEs should also get refactored in the process but there 
>> >> >> >> >>> >> is no need
>> >> >> >> >>> >> to "refactor" it in its current form if it is in fact being
>> >> >> >> >>> >> *completely* rewritten: we can just make sure that the new 
>> >> >> >> >>> >> code is
>> >> >> >> >>> >> written the way we want it to be.
>> >> >> >> >>> >>
>> >> >> >> >>> >> On Sun, 23 Feb 2020 at 19:52, Milan Jolly 
>> >> >> >> >>> >> <[email protected]> wrote:
>> >> >> >> >>> >> >
>> >> >> >> >>> >> > Ok so I have gone through the links suggested and I have 
>> >> >> >> >>> >> > realised that as far as ODE module is concerned, 
>> >> >> >> >>> >> > refactoring is the most important task. But, as far as 
>> >> >> >> >>> >> > that is concerned, I think Mohit Balwani is working on 
>> >> >> >> >>> >> > this for a while and I want to limit any collisions with 
>> >> >> >> >>> >> > my co-contributors. So, I have couple of ideas to work on:
>> >> >> >> >>> >> > 1. Helping to extend the solvers, i.e.implementing a fully 
>> >> >> >> >>> >> > working n-equations solver for constant coefficient 
>> >> >> >> >>> >> > homogeneous systems. This is from the ODE systems map. I 
>> >> >> >> >>> >> > am interested in working on this but I understand that it 
>> >> >> >> >>> >> > might be hard to work upon it while refactoring takes 
>> >> >> >> >>> >> > place. Still, if its possible to work on this and if no 
>> >> >> >> >>> >> > one else has started to work in this direction yet then I 
>> >> >> >> >>> >> > am willing to work for this.
>> >> >> >> >>> >> > 2. Using connected components function implemented by 
>> >> >> >> >>> >> > Oscar Benjamin in 
>> >> >> >> >>> >> > https://github.com/sympy/sympy/pull/16225 to enhance ODE 
>> >> >> >> >>> >> > solvers and computing eigen values faster as mentioned 
>> >> >> >> >>> >> > here https://github.com/sympy/sympy/issues/16207 .
>> >> >> >> >>> >> > 3. This idea is not mentioned in the ideas page and is 
>> >> >> >> >>> >> > something of my own. If there is anything possible, then I 
>> >> >> >> >>> >> > can also work on extending functions like maximum, 
>> >> >> >> >>> >> > minimum, argmax, argmin, etc in calculus module. I have 
>> >> >> >> >>> >> > been working on the issue 
>> >> >> >> >>> >> > https://github.com/sympy/sympy/pull/18550 and I think 
>> >> >> >> >>> >> > there is some scope to extend these functionalities.
>> >> >> >> >>> >> >
>> >> >> >> >>> >> > On Sunday, February 23, 2020 at 1:32:20 AM UTC+5:30, Milan 
>> >> >> >> >>> >> > Jolly wrote:
>> >> >> >> >>> >> >>
>> >> >> >> >>> >> >> Hello everyone,
>> >> >> >> >>> >> >>
>> >> >> >> >>> >> >> My name is Milan Jolly and I am an undergraduate student 
>> >> >> >> >>> >> >> at Indian Institute of Technology, Patna. For the past 2 
>> >> >> >> >>> >> >> month, I have been learning and exploring sympy through 
>> >> >> >> >>> >> >> either contributions, reading documentation or trying 
>> >> >> >> >>> >> >> examples out. This last month I have learned a lot of new 
>> >> >> >> >>> >> >> things thanks to the well designed code-base, the 
>> >> >> >> >>> >> >> structured way this community works and most importantly 
>> >> >> >> >>> >> >> the maintainers who make it work. It has been a pleasure 
>> >> >> >> >>> >> >> to be a part of the community.
>> >> >> >> >>> >> >>
>> >> >> >> >>> >> >> I am interested in participating for GSoC this year and I 
>> >> >> >> >>> >> >> would like to work for this org during the summers if I 
>> >> >> >> >>> >> >> am lucky. I particularly want to work on improving the 
>> >> >> >> >>> >> >> current ODE module as it is given in the idea list. There 
>> >> >> >> >>> >> >> is a lot of work that needs to be taken care of like:
>> >> >> >> >>> >> >> 1. Implementing solvers for solving constant coefficient 
>> >> >> >> >>> >> >> non-homogeneous systems
>> >> >> >> >>> >> >> 2. Solving mixed order ODEs
>> >> >> >> >>> >> >> 3. Adding rearrangements to solve the system
>> >> >> >> >>> >> >>
>> >> >> >> >>> >> >> These are not my ideas but I have taken inspiration from 
>> >> >> >> >>> >> >> the ideas page but I am up for working on these. If 
>> >> >> >> >>> >> >> someone can guide me regarding this then it would be 
>> >> >> >> >>> >> >> really helpful.
>> >> >> >> >>> >> >
>> >> >> >> >>> >> > --
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