Hi,

look at this Maple code.

short_list_prime_factorization_fun.mw

short_list_prime_factorization_fun.pdf

Have a good day

Matthew

Hi,

check out this maple code

positive_odd_integer_factorization_data.pdf

positive_odd_integer_factorization_data.mw

that is all

Regards,

Matthew

Hi,

have some Maple code to share.

prime_triplet_0_4_6.mw

prime_triplet_0_4_6.pdf

Enjoy

Matthew

ps Prime numbers are fun

see https://t5k.org/

Hi,

Don't laugh.

Some of you are not familair with Wagstaff Prime Numbers

see Wolfram Math World

also, this Maple code is esentially, a loop and the isprime() function

for your edification

b

have a look

just wanted to contribute my two cents to the Maple community

good day

We are excited to announce that the Maple Conference will be held Novemeber 5-7, 2025!

Please join us at this free virtual event as it will be an excellent opportunity to meet other members of the Maple community, get the latest news about our products, and hear from the experts about the challenges and opportunities that our technology brings to teaching, learning, and research. More importantly, it's a chance for you to share the work you've been doing with Maple and Maple Learn. 

The Call for Participation is now open. We are inviting submissions of presentation proposals on a range of topics related to Maple, including Maple in education, algorithms and software, and applications. We also encourage submission of proposals related to Maple Learn. 

You can find more information about the themes of the conference and how to submit a presentation proposal at the Call for Participation page. Applications are due July 25, 2025.

After the conference, all accepted presenters and invited speakers will be invited to submit related content to the Maple Transactions journal for consideration.

Registration for attending the conference will open in July.  Watch for further announcements in the coming weeks.

We hope all of you in the Maple Primes community will join us for this event!

Kaska Kowalska
Contributed Program Co-Chair

Thanks to @salim-barzani, I became interested in the Laplace Adomian Decompositon method to solve partial differential equations. It produces a sum of analytical components that converge to the exact solution. Probably it is not that efficient for numerical solutions, but the possibility of finding an exact solution to nonlinear PDEs by finding a formula for the components and therefore the infinite sum is interesting.

The version here covers many of the common forms of PDEs, but is still a work in progress. The method for finding the Adomian polynomials could be improved by using one of the reccurence formulas in the literature. Extension to more PDE forms, ODEs etc are possible and I will attempt some of these before uploading an improved version to the application centre. In the meantime, feedback about bugs, usability etc. are appreciated.

Download Laplace_Adomian_Decomposition_procedure8b.mw

The link below goes to the Proceedings of the Maple 2024 Conference, which includes several articles that will be of interest to the readers of Maple Primes.

There may be one more paper coming in to the proceedings later as per policy; since most things are ready, away we go!

Proceedings of the Maple 2024 Conferenc

In Optical-Illusion--Impossible-Prisms, mcarvalho provides a Maple Learn worksheet to illustrate an intriguing optical illusion.  Here I do that illustration in Maple, and in 3D! 

The graphics below shows 4 bars.  Or is it 3 bars?

Download the worksheet, stare at the graphics for short while to grasp the nature of the optical illusion, and then rotate the 3D graph with the mouse and see the illusion fall apart.

optical-illusion.mw

Geometrical entertainment in the form of rolling without slipping, now inside a torus.
You can also print out the corresponding equations - these graphs are # in the text, but it is better to do this separately from the geometric animation, because textplot3d takes up a lot of resources.

Please consider it not as a Maple program, but simply as an idea for a corresponding algorithm.
for_TORUS_IN_TORUS_for.mw
 

The workbook includes the excel data file but I also included the worksheet and data file separate for versions that can't load workbooks.

Average_World_Temperature_workbook-.maple

Average_World_Temperature_since_1850_worksheet.mw

HadCRUT5.zip

Last week, DeepMind announced their new AlphaEvolve software along with a number of new results improving optimization problems in a number of areas. One of those results is a new, smaller, tensor decomposition for multiplying two 4 × 4 matrices in 48 scalar multiplications, improving on the previous best decomposition which needed 49 multiplications (Strassen 1969). While the DeepMind paper was mostly careful about stating this, when writing the introduction, the authors mistakenly/confusingly wrote:

For 56 years, designing an algorithm with rank less than 49 over any field with characteristic 0 was an open problem. AlphaEvolve is the first method to find a rank-48 algorithm to multiply two 4 × 4 complex-valued matrices

This gives the impression that this new result is the way to multiply 4 × 4 matrices over a field with the fewest number of field multiplications. However, it's not even the case that Strassen's 1969 paper gave a faster way to multiply 4 × 4 matrices. In fact, Winograd's 1968 paper on computing inner products faster could be applied to 4 × 4 matrix multiplication to get a formula using only 48 multiplications. Winograd uses a trick 

which changes two multiplications of ab's into one multiplication of ab's and two multiplications involving only a's or only b's. The latter multiplications can be pre-computed and saved when calculating all the innerproducts in a matrix multiplication

    # i=1..4, 4*2 = 8 multiplications
    p[i] := -A[i, 1]*A[i, 2] - A[i, 3]*A[i, 4];
    # 4*2 = 8 multiplications
    q[i] := -B[1, i]*B[2, i] - B[3, i]*B[4, i];

    # i=1..4, j=1..4, 4*4*2 = 32 multiplications
    C[i,j] = p[i] + q[j] 
            + (A[i,1]+B[2,j])*(A[i,2]+B[1,j])
            + (A[i,3]+B[4,j])*(A[i,4]+B[3,j])

It is simple to verify that C[i,j] = A[i,..] . B[..,j] and that the above formula calculates all of the entries of C=A.B with 8+8+32=48 multiplications.

So, if Winograd achieved a formula of 48 multiplications in 1968, why is the DeepMind result still interesting? Well, the relative shortcoming of Winograd's method is that it only works if the entries of A and B commute, while tensor decomposition formulas for matrix multiplication work even over noncommutative rings. That seems very esoteric, but everyone's favorite noncommutative ring is the ring of n × n matrices. So if a formula applies to a matrix of matrices, that means it gives a recursive formula for matrix multiplication - an tensor decomposition formulas do just that.

The original 1969 Strassen result gave a tensor decomposition of 2 × 2 matrix multiplication using only 7 scalar multiplications (rather than the 8 required by the inner product method) and leads to a recursive matrix multiplication algorithm using O(N^log[2](7)) scalar multiplications. Since then, it has been proved that 7 is the smallest rank tensor decompostion for the 2 × 2 case and so researchers have been interested in the 3x3 and larger cases. Alexandre Sedoglavic at the University of Lille catalogs the best tensor decompositions at https://fmm.univ-lille.fr/ with links to a Maple file with an evaluation formula for each.

The previous best 4 × 4 tensor decomposition was based on using the 2 × 2 Strassen decomposition recursively (2 × 2 matrix of 2 × 2 matrices) which led to 7 2 × 2 matrix multiplications each requiring 7 scalar multiplications, for a total of 49. The new DeepMind result reduces that to 48 scalar multiplications, which leads to a recursive algorithm using O(N^log[4](48)) scalar multiplications: O(N^2.7925) vs. O(N^2.8074) for Strassen. This is a theoretical improvment over Strassen but in 2024 the best known multiplication algorithm has much lower complexity: O(N^2.3713) (see https://arxiv.org/abs/2404.16349). Now, there might be some chance that the DeepMind result could be used in practical implementations, but its reduction in the number of multiplications comes at the cost of many more additions. Before doing any optimizations I counted 1264 additions in the DeepMind tensor, compared to 318 in the unoptimized 4×4 Strassen tensor (which can be optimized to 12*7+4*12=132). Finally, the DeepMind tensor decomposition involves 1/2 and sqrt(-1), so it cannot be used for fields of characteristic 2, or fields without sqrt(-1). Of course, the restriction on characteristic 2 is not a big deal, since the DeepMind team found a 4 × 4 tensor decomposition of rank 47 over GF2 in 2023 (see https://github.com/google-deepmind/alphatensor).

Now if one is only interested in 4 × 4 matrices over a commutative ring, the Winograd result isn't even the best possible. In 1970, Waksman https://ieeexplore.ieee.org/document/1671519 demonstrated an improvement of Winograd's method that used only 46 multiplications as long as the ring allowed division by 2. Waksman's method has since been improved by Rosowski to remove the divisions see https://arxiv.org/abs/1904.07683. Here's that nice compact straight-line program that computes C = A · B in 46 multiplications.

And, here attached are five Maple worksheets that create the explicit formulas for the 4 × 4 matrix methods mentioned in this post, and verify their operation count, and that they are correct.

Strassen_444.mw  Winograd.mw  DM_444.mw Waksman.mw Rosowski.mw

If you are interested in some of the 2022 results, I posted some code to my GitHub account for creating formulas from tensor decompositions, and verifying them on symbolic matrix multiplications: https://github.com/johnpmay/MapleSnippets/tree/main/FastMatrixMultiplication

The clear followup question to all of this is:  does Maple use any of these fast multiplication schemes?  And the answer to that is mostly hidden in low level BLAS code, but generally the answer is: No, the straightforward inner-product scheme for multiplying matrices optimized memory access thus usually ends up being the fastest choice in most cases.

For a long time, triggered by a disagreement with one of my teachers, I wanted to demonstrate that Euler equations are not absolutely necessary to reproduce gyroscopic effects. Back then, there were no computer tools like Maple or programming languages with powerful libraries like Python. Propper calculations by hand (combining Newton’s equations and vector calculus) would have required days without guarantee of immediate success. Overall, costs and risks were too high to go into an academic argument with someone in charge of grading students.

Some years ago, I remembered the unfinished discussion with my teacher and simulated with MapleSim the simple gyroscope with two point masses that I had in mind at the time. It took only 10 minutes to demonstrate that I was right. At least I thought so. As I discovered recently when investigating the intermediate axis theorem, MapleSim derives behind the scenes Euler equations. This devalued the demonstration.

This post is about a second and successful attempt of a demonstration with Maple employing Lagrangian mechanics.  A rotating system of three point masses connected by rigid struts is used. The animation below from the attached Maple worksheet exactly reproduces a simulation of an equivalent T-shaped structure of three identical masses presented here.

Lagrangian Mechanics

The worksheet uses Lagrangian mechanics to derive equations of motion. Only translational energy terms are used in the Lagrangian to prevent Euler equations from being derived. To account for the bound motion of the three point masses, geometric constraints with Lagrange multipliers were added to the Lagrangian L of the system. This lead to a modified Lagrangian L’ that can be used with dedicated Maple commands to derive with little effort a set of Lagrange’s equations of the first kind and the corresponding constraints

(source https://en.wikipedia.org/wiki/Lagrangian_mechanics)

For the system of three point masses the above equations lead to 9 coupled second-order ordinary differential equations (ODEs) and 3 algebraic constraints 

(Maple output from the command Physics,LagrangeEquations)

where x_i, y_i and z_i are the components of the position vectors _i of the masses 1 to 3 and the l_i,j are the constraints between the masses i and j, and b and h are the base and the height of the triangle.

The 12 equations together are also referred to as differential algebraic equations (DAEs). Maple has dedicated solvers for such systems which make implementation easy. The most difficult part is setting the initial conditions for all point masses. In this respect MapleSim is even easier to use since not all initial conditions have to be exactly defined by the user. MapleSim also detects constraints that allow for a simplification of the problem by reducing the number of variables to solve. This leads automatically to 3 instead of 12 equations to be solved. Computational effort is reduced in this way significantly.

Newtonian Mechanics

One could argue now that the above is a demonstration with Lagrangian mechanics and not with Newtonian mechanics. To treat the system in a Newtonian way, the masses must be isolated and internal forces acting on each mass via the struts are applied to each mass and effectively become external forces. This leads to 9 ODEs with 27 unknows

Following actio=reactio for each of the (massless) struts reduces the number of unknows to 18

To solve for the 18 unknows, 9 more relations are required. 3 algebraic constraints that keep the distance between the masses constant have already been listed in the previous section. 6 further algebraic constraints can be established from the fact that the force vectors point towards the opposing mass (see also below).
The effort to solve this system of equations will be even greater but with the benefit of having information about the internal forces in the system. Before making this effort, it is advisable to take a closer look at the equations of motion derived so far.

Forces and the "mysterious" Lagrange Multipliers

Rearranging equations of motion from Lagrangian mechanics to

and comparing this to the equations of motion from Newtonian mechanics yields in vector notation 

or more general for the forces

where the _i are the position vectors of the individual masses and the  are the constraint forces between them.  

In the case of a triangle formed by struts all internal forces must act in the direction of the edges of the triangle. If they would not act this way, opposing pairs of  and   would create a torque around the struts which would lead to an infinite angular acceleration of the massless struts. The above equation confirms this reasoning: The internal forces act in the direction of the difference vectors between the position vectors of the masses (which describe the edges) and scale with l_ij.

The beauty of the Lagrange multipliers in this case is that they hit 3 birds (three components of the vectors ) with one stone. This reduces the number of unknowns.

However, the Lagrange multipliers are somehow mysterious because they do not represent a physical quantity, but they can be used to calculate meaningful and correct physical results.

What makes them even more mysterious is the fact that positions constraints can be expressed in different ways. In the above example the square of the distance between the masses is kept constant instead of the distance. There are many more possibilities to formulate the constraint of constant distance and each of them results in different multipliers l_ij with different units. In principle they should all work equivalently but might not all be usable with dedicated solvers.

According to the above equation, the internal forces in a strut scale with the Lagrange multipliers and the length of the strut. During the back and forth flip of the triangle in the above animation the forces vary which can be appreciated from the l_ij in the following plot. 

Some observations:

• At the start, only the strut between the red and the green masses is tensed by centrifugal forces. This would have been intuitively expected.
• At the start, the broken symmetry in the initial conditions is already visible by the imbalance of the forces in the two other struts.
• At no time two forces are zero
• There is never compression in two struts at the same time. The existence of compression forces renders any attempt to replace the struts by cables useless
• Plotted together in 3D the Lagrange multipliers describe a seemingly perfect circle.

The last observation in particular shows that both the Lagrange multipliers and the IAT still hold some secrets that need to be clarified:

• Is it an exact circle? How to prove that?
• Does a circle appear for all initial conditions and geometries (obtuse isosceles triangles) when the intermediate axis theorem manifests?
• What does the circle represent? Is it a kind of an invariant between the Lagrange multipliers that allows the calculation of one force when the two others are known?
• Is there an analytical representation of the circle as a function of the geometry and the initial conditions?
• What determines the center, the radius and the orientation of the circle?

Conclusion

Overall, the approach of adding non-physical terms that are zero to a physical quantity (the difference between kinetic and potential energy) to derive something meaningful is not obvious at all and underlines the genius of Lagrange. For a system of three bound masses it could be used to calculate internal forces as opposed to the more common use of calculating external constraint forces. Beyond the lack of fully satisfying intuitive explanations, the IAT still offers unanswered scientific questions.

PS.: 

• The exercise was a nice cross-check of MapleSim and Maple.
• dsolve and odeplot are awsome

IAT_without_Euler_equations.mw

My maplet (author: Xavier Cormier) "sudokuvision_multithread3" allows you to generate, solve, and play nxm region sudoku puzzles. It is possible to import lists of puzzles in .csv, .sdm, and .sdk format. It loads a grid in line format. It saves puzzles in .txt and .csv format to import them on Android (Andoku3, OpenSudoku; you must re-save the file in .sdm) and on iOS (SmartSudoku, Good Sudoku). It saves game puzzles (in numbers or colors) and their solution as a .gif image.
Two possible interfaces.
Multithreading is used to generate sudoku puzzles in parallel on multiple threads: we use sequences for the random number and the number of sudokus for each thread:
e.g., [4521,7458]=s1 for the random number and [2,2]=s2 for the number of sudokus. We then generate 2[i] sudokus for the ith thread. with s1[i] as a random integer.
The maplet is compatible with Maple 2021 but not with Maple 2015.

Go to my Maple Cloud link :

https://maple.cloud/app/6283420531032064/sudoku+multithread3?key=8DBF1E6D71EF423B9BE883C27FF0FD892C14B4FCD25B4920A29AFE4E4B4363B8

My maplet (author :Xavier Cormier) studies the partitioning of a set and the decomposition of an integer into a sum.
It can be used in the case of a dice roll.

It determines:
- the number of times each face is obtained for n rolls for an m-sided die.
- the decomposition of an integer nx into the sum of mx integers.
- the partitioning of a set into u non-empty, disjoint pairwise parts.
- the partitions of an integer as well as the dice rolls having a given sum and the probability (equiprobability) of obtaining this sum.

Go to my link on Maple Cloud :

https://maple.cloud/app/6553582941372416/decomposition+d%27un+entier+en+somme+et+partage+d%27une+liste+en+parties+-+app+pour+lanc%C3%A9s+de+d%C3%A9s?key=8533B69313624ADCBE181600B15A1F4F5DDBBFAE86694B479CE492515459A2C9

Hello everyone,

I am creating this post to begin a thread where I will share a series of worksheets on important topics in Complex Analysis, written as part of my notes for my classes. Complex_Analysis_Notes.pdf

The planned sections include:

• Section 1: Infinite Series

• Section 2: Power Series

• Section 3: The Radius of Convergence of a Power Series

• Section 4: The Riemann Zeta Function and the Riemann Hypothesis

• Section 5: The Prime Number Theorem

Each worksheet will include calculations, plots, and examples using Maple to illustrate key ideas.

✅ I plan to upload one worksheet every week to keep a steady pace and allow time for discussion and feedback between posts.

I hope this thread will be helpful both for learning and for deeper exploration.
Feel free to comment, suggest improvements, or ask questions as I post the materials.

Thank you!

Download infinite_series.mw