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In just two weeks, the competition has become both intense and friendly as participants race to climb the team leaderboard, especially in Team Creative, where @Mehdi Dehghan currently leads with 1400+ points, followed by @Vasilis Bellos with 900+ points.
There’s still plenty of time to participate before the contest's main round ends on December 7. Solving just one problem in the contest problem group gives you a chance to win MathWorks T-shirts or socks. Completing the entire problem group not only boosts your odds but also helps your team win.
🎉 Week 2 Winners:
Weekly Prizes for Contest Problem Group Finishers:
@Cephas, @Athi, @Bin Jiang, @Armando Longobardi, @Simone, @Maxi, @Pietro, @Hong Son, @Salvatore, @KARUPPASAMYPANDIYAN M
Weekly Prizes for Contest Problem Group Solvers:
@Greg Herzog, @Anubhav, @AJ Roelofs, @Travis Malouf, @Syed Muhammad Anas Ibrahim
Weekly Prizes for Tips & Tricks Articles:
This week’s prize goes to @Athi for the highly detailed post Solving Systematically The Clueless - Lord Ned in the Game Room.
Comment from the judge:
Shortly after the problem set dropped, several folks recognized that the final problem, "Clueless", was a step above the rest in difficulty. So, not surprisingly, there were a few posts in the discussion boards related to how to tackle this problem. Athi, of the Cool Coders, really dug deep into how the rules and strategies could be turned into an algorithm. There's always more than one way to tackle any difficult programming problem, so it was nice to see some discussion in the comments on different ways you can structure the array that represents your knowledge of who has which cards.
Congratulations to all Week 2 winners! Let’s keep the momentum going!
Developing an application in MATLAB often feels like a natural choice: it offers a unified environment, powerful visualization tools, accessible syntax, and a robust technical ecosystem. But when the goal is to build a compilable, distributable app, the path becomes unexpectedly difficult if your workflow depends on symbolic functions like sym, zeta, or lambertw.
This isn’t a minor technical inconvenience—it’s a structural contradiction. MATLAB encourages the creation of graphical interfaces, input validation, and dynamic visualization. It even provides an Application Compiler to package your code. But the moment you invoke sym, the compiler fails. No clear warning. No workaround. Just: you cannot compile. The same applies to zeta and lambertw, which rely on the symbolic toolbox.
So we’re left asking: how can a platform designed for scientific and technical applications block compilation of functions that are central to those very disciplines?
What Are the Alternatives?
  • Rewrite everything numerically, avoiding symbolic logic—often impractical for advanced mathematical workflows.
  • Use partial workarounds like matlabFunction, which may work but rarely preserve the original logic or flexibility.
  • Switch platforms (e.g., Python with SymPy, Julia), which means rebuilding the architecture and leaving behind MATLAB’s ecosystem.
So, Is MATLAB Still Worth It?
That’s the real question. MATLAB remains a powerful tool for prototyping, teaching, analysis, and visualization. But when it comes to building compilable apps that rely on symbolic computation, the platform imposes limits that contradict its promise.
Is it worth investing time in a MATLAB app if you can’t compile it due to essential mathematical functions? Should MathWorks address this contradiction? Or is it time to rethink our tools?
I’d love to hear your thoughts. Is MATLAB still worth it for serious application development?
In just one week, we have hit an amazing milestone: 500+ players registered and 5000+ solutions submitted! We’ve also seen fantastic Tips & Tricks articles rolling in, making this contest a true community learning experience.
And here’s the best part: you don’t need to be a top-ranked player to win. To encourage more casual and first-time players to jump in, we’re introducing new weekly prizes starting Week 2!
New Casual Player Prizes:
  • 5 extra MathWorks T-shirts or socks will be awarded every week.
  • All you need to qualify is to register and solve one problem in the Contest Problem Group.
Jump in, try a few problems, and don’t be shy to ask questions in your team’s channel. You might walk away with a prize!
Week 1 Winners:
Weekly Prizes for Contest Problem Group Finishers:
@Mazhar, @Julien, @Mohammad Aryayi, @Pawel, @Mehdi Dehghan, @Christian Schröder, @Yolanda, @Dev Gupta, @Tomoaki Takagi, @Stefan Abendroth
Weekly Prizes for Tips & Tricks Articles:
Week 1 winner for best Tips & Tricks Articles is @Vasilis Bellos.
Contest problems author @Matt Tearle commented:
We had a lot of people share useful tips (including some personal favorite MATLAB tricks). But Vasilis Bellos went *deep* into the Bridges of Nedsburg problem. Fittingly for a Creative Coder, his post was innovative and entertaining, while also cleverly sneaking in some hints on a neat solution method that wasn't advertised in the problem description.
Congratulations to all Week 1 winners! Prizes will be awarded after the contest ends. Let’s keep the momentum going!
What a fantastic start to Cody Contest 2025! In just 2 days, over 300 players joined the fun, and we already have our first contest group finishers. A big shoutout to the first finisher from each team:
  • Team Creative Coders: @Mehdi Dehghan
  • Team Cool Coders: @Pawel
  • Team Relentless Coders: @David Hill
  • 🏆 First finisher overall: Mehdi Dehghan
Other group finishers: @Bin Jiang (Relentless), @Mazhar (Creative), @Vasilis Bellos (Creative), @Stefan Abendroth (Creative), @Armando Longobardi (Cool), @Cephas (Cool)
Kudos to all group finishers! 🎉
Reminder to finishers: The goal of Cody Contest is learning together. Share hints (not full solutions) to help your teammates complete the problem group. The winning team will be the one with the most group finishers — teamwork matters!
To all players: Don’t be shy about asking for help! When you do, show your work — include your code, error messages, and any details needed for others to reproduce your results.
Keep solving, keep sharing, and most importantly — have fun!
Title: Looking for Internship Guidance as a Beginner MATLAB/Simulink Learner
Hello everyone,
I’m a Computer Science undergraduate currently building a strong foundation in MATLAB and Simulink. I’m still at a beginner level, but I’m actively learning every day and can work confidently once I understand the concepts. Right now I’m focusing on MATLAB modeling, physics simulation, and basic control systems so that I can contribute effectively to my current project.
I’m part of an Autonomous Underwater Vehicle (AUV) team preparing for the Singapore AUV Challenge (SAUVC). My role is in physics simulation, controls, and navigation, and MATLAB/Simulink plays a major role in that pipeline. I enjoy physics and mathematics deeply, which makes learning modeling and simulation very exciting for me.
On the coding side, I practice competitive programming regularly—
• Codeforces rating: ~1200
• LeetCode rating: ~1500
So I'm comfortable with logic-building and problem solving. What I’m looking for:
I want to know how a beginner like me can start applying for internships related to MATLAB, Simulink, modeling, simulation, or any engineering team where MATLAB is widely used (including companies outside MathWorks).
I would really appreciate advice from the community on:
  • What skills should I strengthen first?
  • Which MATLAB/Simulink toolboxes are most important for beginners aiming toward simulation/control roles?
  • What small projects or portfolio examples should I build to improve my profile?
  • What is the best roadmap to eventually become a good candidate for internships in this area?
Any guidance, resources, or suggestions would be extremely helpful for me.
Thank you in advance to everyone who shares their experience!
The main round of Cody Contest 2025 kicks off today! Whether you’re a beginner or a seasoned solver, now’s your time to shine.
Here’s how to join the fun:
  • Pick your team — choose one that matches your coding personality.
  • Solve Cody problems — gain points and climb the leaderboard.
  • Finish the Contest Problem Group — help your team win and unlock chances for weekly prizes by finishing the Cody Contest 2025 problem group.
  • Share Tips & Tricks — post your insights to win a coveted MathWorks Yeti Bottle.
  • Bonus Round — 2 players from each team will be invited to a fun live code-along event!
  • Watch Party – join the big watch event to see how top players tackle Cody problems
Contest Timeline:
  • Main Round: Nov 10 – Dec 7, 2025
  • Bonus Round: Dec 8 – Dec 19, 2025
Big prizes await — MathWorks swag, Amazon gift cards, and shiny virtual badges!
We look forward to seeing you in the contest — learn, compete, and have fun!
David
David
Last activity on 6 Nov 2025 at 20:47

Parallel Computing Onramp is here!

Parallel Computing Onramp is here! This free, one-hour self-paced course teaches the basics of running MATLAB code in parallel using multiple CPU cores, helping users speed up their code and write code that handles information efficiently.
Remember, Onramps are free for everyone - give the new course a try if you're curious. Let us know what you think of it by replying below.
https://matlabacademy.mathworks.com/details/parallel-computing-onramp/orpc
From my experience, MATLAB's Deep Learning Toolbox is quite user-friendly, but it still falls short of libraries like PyTorch in many respects. Most users tend to choose PyTorch because of its flexibility, efficiency, and rich support for many mathematical operators. In recent years, the number of dlarray-compatible mathematical functions added to the toolbox has been very limited, which makes it difficult to experiment with many custom networks. For example, svd is currently not supported for dlarray inputs.
This link (List of Functions with dlarray Support - MATLAB & Simulink) lists all functions that support dlarray as of R2026a — only around 200 functions (including toolbox-specific ones). I would like to see support for many more fundamental mathematical functions so that users have greater freedom when building and researching custom models. For context, the core MATLAB mathematics module contains roughly 600 functions, and many application domains build on that foundation.
I hope MathWorks will prioritize and accelerate expanding dlarray support for basic math functions. Doing so would significantly increase the Deep Learning Toolbox's utility and appeal for researchers and practitioners.
Thank you.
I'm developing a comprehensive MATLAB programming course and seeking passionate co-trainers to collaborate!
Why MATLAB Matters:Many people underestimate MATLAB's significance in:
  • Communication systems
  • Signal processing
  • Mathematical modeling
  • Engineering applications
  • Scientific computing
Course Structure:
  1. Foundation Module: MATLAB basics and fundamentals
  2. Image Processing: Practical applications and techniques
  3. Signal Processing: Analysis and implementation
  4. Machine Learning: ML algorithms using MATLAB
  5. Hands-on Learning: Projects, assignments.
What I'm Looking For:
  • Enthusiastic educators willing to share knowledge
  • Experience in any MATLAB application area
  • Commitment to collaborative teaching
Interested in joining as a co-trainer? Please comment below or reach out directly!
Online Doc + System Browser
11%
Online Doc + Dedicated Browser
11%
Offline Doc +System Browser
11%
Offline Doc + Dedicated Browser
23%
Hybrid Approach (Support All Modes)
23%
User-Definable / Fully Configurable
20%
35 votes
I'm working on training neural networks without backpropagation / automatic differentiation, using locally derived analytic forms of update rules. Given that this allows a direct formula to be derived for the update rule, it removes alot of the overhead that is otherwise required from automatic differentiation.
However, matlab's functionalities for neural networks are currently solely based around backpropagation and automatic differentiation, such as the dlgradient function and requiring everything to be dlarrays during training.
I have two main requests, specifically for functions that perform a single operation within a single layer of a neural network, such as "dlconv", "fullyconnect", "maxpool", "avgpool", "relu", etc:
  • these functions should also allow normal gpuArray data instead of requiring everything to be dlarrays.
  • these functions are currently designed to only perform the forward pass. I request that these also be designed to perform the backward pass if user requests. There can be another input user flag that can be "forward" (default) or "backward", and then the function should have all the necessary inputs to perform that operation (e.g. for "avgpool" forward pass it only needs the avgpool input data and the avgpool parameters, but for the "avgpool" backward pass it needs the deriviative w.r.t. the avgpool output data, the avgpool parameters, and the original data dimensions). I know that there is a maxunpool function that achieves this for maxpool, but it has significant issues when trying to use it this way instead of by backpropagation in a dlgradient type layer, see (https://www.mathworks.com/matlabcentral/answers/2179587-making-a-custom-way-to-train-cnns-and-i-am-noticing-that-avgpool-is-significantly-faster-than-maxpo?s_tid=srchtitle).
I don't know how many people would benefit from this feature, and someone could always spend their time creating these functionalities themselves by matlab scripts, cuDNN mex, etc., but regardless it would be nice for matlab to have this allowable for more customizable neural net training.
I recently published this blog post about resources to help people learn MATLAB https://blogs.mathworks.com/matlab/2025/09/11/learning-matlab-in-2025/
What are your favourite MATLAB learning resources?
Edit 15 Oct 2025: Removed incorrect code. Replaced symmatrix2sym and symfunmatrix2symfun with sym and symfun respectively (latter supported as of 2024b).
The Symbolic Math Toolbox does not have its own dot and and cross functions. That's o.k. (maybe) for garden variety vectors of sym objects where those operations get shipped off to the base Matlab functions
x = sym('x',[3,1]); y = sym('y',[3,1]);
which dot(x,y)
/MATLAB/toolbox/matlab/specfun/dot.m
dot(x,y)
ans = 
which cross(x,y)
/MATLAB/toolbox/matlab/specfun/cross.m
cross(x,y)
ans = 
But now we have symmatrix et. al., and things don't work as nicely
clearvars
x = symmatrix('x',[3,1]); y = symmatrix('y',[3,1]);
z = symmatrix('z',[1,1]);
sympref('AbbreviateOutput',false);
dot() expands the result, which isn't really desirable for exposition.
eqn = z == dot(x,y)
eqn = 
Also, dot() returns the the result in terms of the conjugate of x, which can't be simplifed away at the symmatrix level
assumeAlso(sym(x),'real')
class(eqn)
ans = 'symmatrix'
try
eqn = z == simplify(dot(x,y))
catch ME
ME.message
end
ans = 'Undefined function 'simplify' for input arguments of type 'symmatrix'.'
To get rid of the conjugate, we have to resort to sym
eqn = simplify(sym(eqn))
eqn = 
but again we are in expanded form, which defeats the purpose of symmatrix (et. al.)
But at least we can do this to get a nice equation
eqn = z == x.'*y
eqn = 
dot errors with symfunmatrix inputs
clearvars
syms t real
x = symfunmatrix('x(t)',t,[3,1]); y = symfunmatrix('y(t)',t,[3,1]);
try
dot(x,y)
catch ME
ME.message
end
ans = 'Invalid argument at position 2. Symbolic function is evaluated at the input arguments and does not accept colon indexing. Instead, use FORMULA on the function and perform colon indexing on the returned output.'
Cross works (accidentally IMO) with symmatrix, but expands the result, which isn't really desirable for exposition
clearvars
x = symmatrix('x',[3,1]); y = symmatrix('y',[3,1]);
z = symmatrix('z',[3,1]);
eqn = z == cross(x,y)
eqn = 
And it doesn't work at all if an input is a symfunmatrix
syms t
w = symfunmatrix('w(t)',t,[3,1]);
try
eqn = z == cross(x,w);
catch ME
ME.message
end
ans = 'A and B must be of length 3 in the dimension in which the cross product is taken.'
In the latter case we can expand with
eqn = z == cross(sym(x),symfun(w)) % x has to be converted
eqn(t) = 
But we can't do the same with dot (as shown above, dot doesn't like symfun inputs)
try
eqn = z == dot(sym(x),symfun(w))
catch ME
ME.message
end
ans = 'Invalid argument at position 2. Symbolic function is evaluated at the input arguments and does not accept colon indexing. Instead, use FORMULA on the function and perform colon indexing on the returned output.'
Looks like the only choice for dot with symfunmatrix is to write it by hand at the matrix level
x.'*w
ans(t) = 
or at the sym/symfun level
sym(x).'*symfun(w) % assuming x is real
ans(t) = 
Ideally, I'd like to see dot and cross implemented for symmatrix and symfunmatrix types where neither function would evaluate, i.e., expand, until both arguments are subs-ed with sym or symfun objects of appropriate dimension.
Also, it would be nice if symmatrix could be assumed to be real. Is there a reason why being able to do so wouldn't make sense?
try
assume(x,'real')
catch ME
ME.message
end
ans = 'Undefined function 'assume' for input arguments of type 'symmatrix'.'
Baranitharan
Baranitharan
Last activity on 8 Oct 2025

Exploring matlab learners mindset

Share your learning starting trouble experience of Matlab.. Looking forward for more answers..
Helllo all
I write The MATLAB Blog and have covered various enhancements to MATLAB's ODE capabilities over the last couple of years. Here are a few such posts
Everyone in this community has deeply engaged with all of these posts and given me lots of ideas for future enhancements which I've dutifully added to our internal enhancment request database.
Because I've asked for so much in this area, I was recently asked if there's anything else we should consider in the area of ODEs. Since all my best ideas come from all of you, I'm asking here....
So. If you could ask for new and improved functionality for solving ODEs with MATLAB, what would it be and (ideally) why?
Cheers,
Mike
Something that I periodically wonder about is whether an integration with the Rubi integration rules package would improve symbolic integration in Matlab's Symbolic Toolbox. The project is open-source with an MIT-licensed, has a Mathematica implementation, and supposedly SymPy is working on an implementation. Much of my intrigue comes from this 2022 report that compared the previous version of Rubi (4.16.1) against various CAS systems, including Matlab 2021a (Mupad):
While not really an official metric for Rubi, this does "feel" similar to my experience computing symbolic integrals in Matlab Symbolic Toolbox vs Maple/Mathematica. What do y'all think?
Collin
Collin
Last activity on 5 Oct 2025

Very good Tech Support

Yesterday I had an urgent service call for MatLab tech support. The Mathworks technician on call, Ivy Ngyuen, helped fix the problem. She was very patient and I truly appreciate her efforts, which resolved the issue. Thank you.
Check out how these charts were made with polar axes in the Graphics and App Building blog's latest article "Polar plots with patches and surface".