Tootfinder

"Most transformation assumes you throw out what's not working and start anew. That's not transformation. That's replacement. Real transformation requires presencing. Deep listening to what is trying to emerge from within the system."
#OttoScharmer
#LernenImWandel

The Deficit Reflex: Why Change Management Keeps Solving The Wrong Problem
Change management keeps failing, even when execution is disciplined. The problem isn’t resistance. It’s the reflex leaders use to decide what needs fixing.

An inexact semismooth Newton-Krylov method for semilinear elliptic optimal control problem
Shiqi Chen, Xuesong Chen
https://arxiv.org/abs/2511.10058 https://arxiv.org/pdf/2511.10058 https://arxiv.org/html/2511.10058
arXiv:2511.10058v1 Announce Type: new
Abstract: An inexact semismooth Newton method has been proposed for solving semi-linear elliptic optimal control problems in this paper. This method incorporates the generalized minimal residual (GMRES) method, a type of Krylov subspace method, to solve the Newton equations and utilizes nonmonotonic line search to adjust the iteration step size. The original problem is reformulated into a nonlinear equation through variational inequality principles and discretized using a second-order finite difference scheme. By leveraging slanting differentiability, the algorithm constructs semismooth Newton directions and employs GMRES method to inexactly solve the Newton equations, significantly reducing computational overhead. A dynamic nonmonotonic line search strategy is introduced to adjust stepsizes adaptively, ensuring global convergence while overcoming local stagnation. Theoretical analysis demonstrates that the algorithm achieves superlinear convergence near optimal solutions when the residual control parameter $\eta_k$ approaches to 0. Numerical experiments validate the method's accuracy and efficiency in solving semilinear elliptic optimal control problems, corroborating theoretical insights.
toXiv_bot_toot

Why Your Problem-Solving Approach Keeps Failing (Complicated vs Complex)
https://www.youtube.com/watch?v=w3Keu9Gcl0g

#Applied #cryptography cannot solve a #security problem. It can only convert a security problem into a key-management problem.
Corollary: If you aren’t actually solving the key-management problem, yo…

VCs fund companies who get on the latest hype train. Solving a real user problem is a secondary concern; "product market fit" is seen as something you do only after you build, a sales problem.
But when the product doesn't solve any real problems, showing "progress" to your board is impossible!
So managers invent metrics. Many people are going to come back to the office this week and set goals based on what activity is easiest to measure, rather than what i…

My almost 4 year old wakes us up yelling: I need to go potty, but I have a banana in my hand.
My partner: put the banana on the table and go to the potty
My almost 4 year old: ok!
Problem solving
#kidposting

I'm listening to the most recent episode of #YouAreNotSoSmart, David McRaney's podcast @…. It's about the Trolley problem and morality.
They keep talking about a variation of the trolley problem where, instead of pulling the lever t…

YANSS 327 – How save ourselves from ourselves by solving the trolley problem inside us all
Philosopher, neuroscientist, and psychologist, Joshua Greene tells us how the brain generates morality and how his research may have solved the infamous trolley problem, and in so doing created a w…

from my link log —
Solving the Partridge square packing problem using MiniZinc.
https://zayenz.se/blog/post/partridge-packing/
saved 2025-11-26 https:/…

Las Vegas Raiders announce 2025 Inspire Change Changemaker https://www.raiders.com/news/las-vegas-raiders-announce-2025-inspire-change-changemaker

Convergence analysis of inexact MBA method for constrained upper-$\mathcal{C}^2$ optimization problems
Ruyu Liu, Shaohua Pan
https://arxiv.org/abs/2511.09940 https://arxiv.org/pdf/2511.09940 https://arxiv.org/html/2511.09940
arXiv:2511.09940v1 Announce Type: new
Abstract: This paper concerns a class of constrained optimization problems in which, the objective and constraint functions are both upper-$\mathcal{C}^2$. For such nonconvex and nonsmooth optimization problems, we develop an inexact moving balls approximation (MBA) method by a workable inexactness criterion for the solving of subproblems. By leveraging a global error bound for the strongly convex program associated with parametric optimization problems, we establish the full convergence of the iterate sequence under the partial bounded multiplier property (BMP) and the Kurdyka-{\L}ojasiewicz (KL) property of the constructed potential function, and achieve the local convergence rate of the iterate and objective value sequences if the potential function satisfies the KL property of exponent $q\in[1/2,1)$. A verifiable condition is also provided to check whether the potential function satisfies the KL property of exponent $q\in[1/2,1)$ at the given critical point. To the best of our knowledge, this is the first implementable inexact MBA method with a full convergence certificate for the constrained nonconvex and nonsmooth optimization problem.
toXiv_bot_toot

Measuring dissimilarity between convex cones by means of max-min angles
Welington de Oliveira, Valentina Sessa, David Sossa
https://arxiv.org/abs/2511.10483 https://arxiv.org/pdf/2511.10483 https://arxiv.org/html/2511.10483
arXiv:2511.10483v1 Announce Type: new
Abstract: This work introduces a novel dissimilarity measure between two convex cones, based on the max-min angle between them. We demonstrate that this measure is closely related to the Pompeiu-Hausdorff distance, a well-established metric for comparing compact sets. Furthermore, we examine cone configurations where the measure admits simplified or analytic forms. For the specific case of polyhedral cones, a nonconvex cutting-plane method is deployed to compute, at least approximately, the measure between them. Our approach builds on a tailored version of Kelley's cutting-plane algorithm, which involves solving a challenging master program per iteration. When this master program is solved locally, our method yields an angle that satisfies certain necessary optimality conditions of the underlying nonconvex optimization problem yielding the dissimilarity measure between the cones. As an application of the proposed mathematical and algorithmic framework, we address the image-set classification task under limited data conditions, a task that falls within the scope of the \emph{Few-Shot Learning} paradigm. In this context, image sets belonging to the same class are modeled as polyhedral cones, and our dissimilarity measure proves useful for understanding whether two image sets belong to the same class.
toXiv_bot_toot

Las Vegas Raiders announce 2025 Inspire Change Changemaker https://www.raiders.com/news/las-vegas-raiders-announce-2025-inspire-change-changemaker

Flapping Airplanes, an AI research lab "devoted to solving the data efficiency problem", raised $180M at a $1.5B valuation from GV, Sequoia, Index, and others (Kate Clark/Wall Street Journal)
https://www.

Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
Ilyas Fatkhullin, Niao He, Guanghui Lan, Florian Wolf
https://arxiv.org/abs/2511.10626 https://arxiv.org/pdf/2511.10626 https://arxiv.org/html/2511.10626
arXiv:2511.10626v1 Announce Type: new
Abstract: Constrained non-convex optimization is fundamentally challenging, as global solutions are generally intractable and constraint qualifications may not hold. However, in many applications, including safe policy optimization in control and reinforcement learning, such problems possess hidden convexity, meaning they can be reformulated as convex programs via a nonlinear invertible transformation. Typically such transformations are implicit or unknown, making the direct link with the convex program impossible. On the other hand, (sub-)gradients with respect to the original variables are often accessible or can be easily estimated, which motivates algorithms that operate directly in the original (non-convex) problem space using standard (sub-)gradient oracles. In this work, we develop the first algorithms to provably solve such non-convex problems to global minima. First, using a modified inexact proximal point method, we establish global last-iterate convergence guarantees with $\widetilde{\mathcal{O}}(\varepsilon^{-3})$ oracle complexity in non-smooth setting. For smooth problems, we propose a new bundle-level type method based on linearly constrained quadratic subproblems, improving the oracle complexity to $\widetilde{\mathcal{O}}(\varepsilon^{-1})$. Surprisingly, despite non-convexity, our methodology does not require any constraint qualifications, can handle hidden convex equality constraints, and achieves complexities matching those for solving unconstrained hidden convex optimization.
toXiv_bot_toot

I'll be in the #fosdem translations dev room this afternoon, speaking about something completely unrelated to my usual topics.
Don't expect a ready to use project though, it's more about sharing a story of creative problem solving. :blobcatartist:

@… I wonder if anyone at Guardian Games has experience solving this problem. I’d reach out to them and ask: https://ggportland.com/portland/

Lastly, whenever I do work with people through a specific case of them having felt like they needed to rely on an LLM, it often goes like this.
They feel guilty and ashamed.
They explain how impossible getting that task done felt with their time and energy constraints.
Yet when I talk them through other ways of solving the same problem, often we end up completing the work much quicker than it even took them to prompt the damn LLM to begin with.
And at the end, I have often seen relief - as if the person has forgotten that there are ways to work quickly while trusting their own brain, getting help in collaboration with another person rather than from a machine.
I do kind of love seeing someone realize that the AI they thought was saving them time actually caused more hassle and stress than it was worth. And that there’s a better way.

Came across this 2010 blog post about mindfulness in computing and so much of these behaviors have only intensified to new extremes with LLM usage. So much so that not only is the process of software creation being quickly supplanted by prompts and (stochastic) "search" assemblies, but more generally the kind of mindfulness talked about in the post (here meaning thinking through & solving a problem yourself[1]) is now being openly discouraged by industry and forcefully delegate…

“The case for upzoning is relatively solid but deeply underwhelming as a standalone position. The upshot is that everyone is at least partly right: Upzoning can address the shortfall in supply. But it won’t come close to solving the housing crisis alone. Re-enter: public housing.”
https://

The Solution to the Affordable Housing Problem That No One Wants to Discuss
There are new ideas for fixing this. And one very old one.

Friday Links 26-04
It is a bit sad that he is leaving In Our Time, but I enjoyed the interview with Melvyn Bragg.
The blog post about curiosity as a leader is short and great.
https://christof.damian.net…

🦝 Raccoons break into liquor stores, scale skyscrapers and pick locks – studying their clever brains can clarify human intelligence, too
https://theconversation.com/raccoons-b

Raccoons break into liquor stores, scale skyscrapers and pick locks – studying their clever brains can clarify human intelligence, too
Trash pandas’ talent for escaping via lab vents may frustrate researchers, but their problem-solving skills make their brains a fascinating area of research.

So in another dream I just woke up from, I was talking to someone about "the idea problem" (that it's becoming harder to monitize ideas, from a vox article written by an AI cooked reporter).
https://www.iheart.com/podcast/105-it-could-happen-here-30717896/episode/executive-disorder-white-house-weekly-46-313675864/
Basically, I was arguing that the majority of inventions target men because patriarchy puts economic control in men's hands. As men have started to help more with childcare, there have been more inventions related to childcare. (I don't have any idea if this is true. Seems legit, but I'm just relating my dream. I think I was also oversimplifying a bit to "men" and "women" because of my audience, but anyway it was a dream.) There's actually more low-hanging fruit, I pointed out, related to making care work easier.
So I argued that the real problem was a failure to invest in research into solving that problem. Today there are all these boondoggles built around killing people. What if, instead of all this government research into killing people, we dumped a ton of money into making it easier to support a household? That would be great for the economy. (Being asleep, I seem to have forgotten that working people need money.)
In the blur of being just awake I started thinking about how you could kickstart the US economy by taking the money from the AI boondoggle and other autonomous murder bots and create something like a program to build robots for housekeepers. You'd still be funding tech with government money, so the same horrible people get paid, but you're now actually solving real problems. It wouldn't even matter if it was a boondoggle, honestly. Just dumping money into something other than murdering people is good enough.
I imagined first if there was a program to fund a robot housecleaner, like robot dog with AI some laundry pickup, that would be provided, free of charge, to help people with children. It would work the same as the military boondoggle where a private company makes the government buy a piece of hardware from them and then also pay them to service it for some number of years. But instead of that hardware sitting around waiting to kill someone, it would be getting brought to people's houses to help them.
Then I thought, hey, you could even boost the economy more if you just had government funding for doulas and housecleaners and paid them a living wage. Hey, you could really kickstart the economy by nationalizing healthcare and including doula support as part of all births. Oh, and you could also just include the optional household help for families with children until the kids turn 18.
None of this is perfect (I don't actually think most of this is possible from any state), but the point is that it's actually wildly easy to figure out all kinds of ways to invest in the economy and monitize ideas as long as you aren't entirely focused on the same old "make money from spying on people and killing them." Funny that. Like they said in the podcast, maybe "finding ideas" isn't the problem.
Hope you enjoyed the weird semi-awake brain dump/rant.

Why Senior Engineers Let Bad Projects Fail
https://lalitm.com/post/why-senior-engineers-let-bad-projects-fail/

Is #AI really just dumb statistics? "Olympiad-level physics problem-solving presents a significant challenge for both humans and artificial intelligence (AI), as it requires a sophisticated integration of precise calculation, abstract reasoning, and a fundamental grasp of physical principles," says the (abstract of the) paper https://arxiv.org/abs/2511.10515: "The Chinese Physics Olympiad (CPhO), renowned for its complexity and depth, serves as an ideal and rigorous testbed for these advanced capabilities. In this paper, we introduce LOCA-R (LOgical Chain Augmentation for Reasoning), an improved version of the LOCA framework adapted for complex reasoning, and apply it to the CPhO 2025 theory examination. LOCA-R achieves a near-perfect score of 313 out of 320 points, solidly surpassing the highest-scoring human competitor and significantly outperforming all baseline methods." Oops ...?

software that people make for themselves may look like spaghetti to a professional, but it's solving the right problem.

In Massachusetts, a startup is transforming the cement industry after developing a fossil fuel-free production system, significantly reducing carbon emissions.
https://triplepundit.com/2025/sublime-systems-cement-carbon-dioxide-emissions/