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#Optimization

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रञ्जित (Ranjit Mathew)<p>Great 👌🏽:</p><p>“Strategies For Very Fast Lexers”, Matteo / ‘xnacly’ (<a href="https://xnacly.me/posts/2025/fast-lexer-strategies/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">xnacly.me/posts/2025/fast-lexe</span><span class="invisible">r-strategies/</span></a>).</p><p>Via HN: <a href="https://news.ycombinator.com/item?id=44560871" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.ycombinator.com/item?id=4</span><span class="invisible">4560871</span></a></p><p>On Lobsters: <a href="https://lobste.rs/s/75zw2o/strategies_for_very_fast_lexers" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">lobste.rs/s/75zw2o/strategies_</span><span class="invisible">for_very_fast_lexers</span></a></p><p><a href="https://mastodon.social/tags/Compilers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Compilers</span></a> <a href="https://mastodon.social/tags/Lexers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Lexers</span></a> <a href="https://mastodon.social/tags/Tokenizers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Tokenizers</span></a> <a href="https://mastodon.social/tags/LexicalAnalyzers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LexicalAnalyzers</span></a> <a href="https://mastodon.social/tags/Speed" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Speed</span></a> <a href="https://mastodon.social/tags/C" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>C</span></a> <a href="https://mastodon.social/tags/Programming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Programming</span></a> <a href="https://mastodon.social/tags/Efficiency" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Efficiency</span></a> <a href="https://mastodon.social/tags/Optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Optimization</span></a> <a href="https://mastodon.social/tags/PLDI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PLDI</span></a></p>
Eric Maugendre<p>Logistic regression may be used for classification.</p><p>In order to preserve the convex nature for the loss function, a log-loss cost function has been designed for logistic regression. This cost function extremes at labels True and False.</p><p>The gradient for the loss function of logistic regression comes out to have the same form of terms as the gradient for the Least Squared Error.</p><p>More: <a href="https://www.baeldung.com/cs/gradient-descent-logistic-regression" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">baeldung.com/cs/gradient-desce</span><span class="invisible">nt-logistic-regression</span></a></p><p><a href="https://hachyderm.io/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a> <a href="https://hachyderm.io/tags/algebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>algebra</span></a> <a href="https://hachyderm.io/tags/linearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearAlgebra</span></a> <a href="https://hachyderm.io/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</span></a> <a href="https://hachyderm.io/tags/maths" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>maths</span></a> <a href="https://hachyderm.io/tags/mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mathematics</span></a> <a href="https://hachyderm.io/tags/mathStodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mathStodon</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/dataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataScience</span></a> <a href="https://hachyderm.io/tags/machineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machineLearning</span></a> <a href="https://hachyderm.io/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a> <a href="https://hachyderm.io/tags/neuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuralNetworks</span></a> <a href="https://hachyderm.io/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NLP</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/models" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>models</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/logisticRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>logisticRegression</span></a> <a href="https://hachyderm.io/tags/logLoss" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>logLoss</span></a> <a href="https://hachyderm.io/tags/sigmoid" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sigmoid</span></a> <a href="https://hachyderm.io/tags/classification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>classification</span></a> <a href="https://hachyderm.io/tags/differentialCalculus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>differentialCalculus</span></a> <a href="https://hachyderm.io/tags/loss" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>loss</span></a></p>
Danish Akhtar<p>If the iPhone has a Silicon Carbon Battery, and if all hardware components, especially Apple's latest Bionic chip and display, are designed to maximize battery life, and if all apps and iOS are heavily optimized for enhanced battery life, and if grayscale mode and dark mode are on at all times, how many days could the battery of this iPhone last on a full charge?</p><p><a href="https://mastodon.social/tags/Mastodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Mastodon</span></a> <a href="https://mastodon.social/tags/tech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tech</span></a> <a href="https://mastodon.social/tags/technology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>technology</span></a> <a href="https://mastodon.social/tags/apple" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>apple</span></a> <a href="https://mastodon.social/tags/iOS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>iOS</span></a> <a href="https://mastodon.social/tags/battery" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>battery</span></a> <a href="https://mastodon.social/tags/whatif" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>whatif</span></a> <a href="https://mastodon.social/tags/BatteryLife" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BatteryLife</span></a> <a href="https://mastodon.social/tags/opinion" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opinion</span></a> <a href="https://mastodon.social/tags/insights" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>insights</span></a> <a href="https://mastodon.social/tags/fediverse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>fediverse</span></a> <a href="https://mastodon.social/tags/Optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Optimization</span></a> <a href="https://mastodon.social/tags/iPhone" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>iPhone</span></a> <a href="https://mastodon.social/tags/explore" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>explore</span></a></p>
éric 🚲 🇪🇺 :emacs:<p>New blog post, the third in a series on the development of an optimization based design tool for heat exchanger network synthesis (aka design):</p><p><a href="https://www.homepages.ucl.ac.uk/~ucecesf/blog/20250814.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">homepages.ucl.ac.uk/~ucecesf/b</span><span class="invisible">log/20250814.html</span></a></p><p><a href="https://fediscience.org/tags/HeatExchangerNetworkDesign" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HeatExchangerNetworkDesign</span></a> <a href="https://fediscience.org/tags/HeatExchangerNetworkSynthesis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HeatExchangerNetworkSynthesis</span></a> <a href="https://fediscience.org/tags/HENS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HENS</span></a> <a href="https://fediscience.org/tags/ProcessSystemsEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProcessSystemsEngineering</span></a> <a href="https://fediscience.org/tags/PSE" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PSE</span></a> <a href="https://fediscience.org/tags/Optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Optimization</span></a> <a href="https://fediscience.org/tags/Visualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Visualization</span></a></p>
Data Science<p>This looks helpful: <a href="https://tysonbarrett.com/tidyfast/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">tysonbarrett.com/tidyfast/</span><span class="invisible"></span></a> <a href="https://genomic.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://genomic.social/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a></p>
arya dradjica<p>I wrote a work-stealing task queue library for Rust! It's called <code>takeaway</code>, and I just published a version I think is ready for use. The only popular task queue library out there (for Rust) is <code>crossbeam-deque</code>; compared to it, <code>takeaway</code> provides a higher-level API with a lot more features. I wrote it as part of my very-very-WIP Rust compiler, which needed the unique feature of task prioritization; <code>takeaway</code>'s since grown a lot, and manages competitive (if not better) performance to <code>crossbeam-deque</code>! You can find it at <a href="https://crates.io/crates/takeaway" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">crates.io/crates/takeaway</span><span class="invisible"></span></a>; I've also written a blog post about the design and implementation process, at <a href="https://bal-e.org/speed/krabby/takeaway" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">bal-e.org/speed/krabby/takeaway</span><span class="invisible"></span></a>. If you're writing a performance-intensive, task-based program in Rust, or if you're already using <code>crossbeam-deque</code>, please check it out.</p><p><a href="https://tech.lgbt/tags/rust" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rust</span></a> <a href="https://tech.lgbt/tags/programming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>programming</span></a> <a href="https://tech.lgbt/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a> <a href="https://tech.lgbt/tags/takeaway" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>takeaway</span></a></p>
Data Science<p>Do you need better performance than what the standard <a href="https://genomic.social/tags/tidyverse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tidyverse</span></a> functions have? {collapse} might be worth a look: <a href="https://sebkrantz.github.io/collapse/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">sebkrantz.github.io/collapse/</span><span class="invisible"></span></a> <a href="https://genomic.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://genomic.social/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a></p>
Ainiriand<p>Everything you need to get lost in the art of software optimization: <a href="https://www.agner.org/optimize/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">agner.org/optimize/</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/software" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>software</span></a> <a href="https://mastodon.social/tags/cpu" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cpu</span></a> <a href="https://mastodon.social/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>optimization</span></a> <a href="https://mastodon.social/tags/rustlang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rustlang</span></a> <a href="https://mastodon.social/tags/hft" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>hft</span></a></p>
éric 🚲 🇪🇺 :emacs:<p>New blog post just published. This is the second in the series on heat exchanger network design.</p><p>The post presents a simple, greedy (but not excessively so 🤔), algorithm for the layout of line segments in a two dimensional representation of the hot and cold streams for a network. The use of an evolutionary optimization method for finding good designs is considered.</p><p><a href="https://www.homepages.ucl.ac.uk/~ucecesf/blog/20250806.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">homepages.ucl.ac.uk/~ucecesf/b</span><span class="invisible">log/20250806.html</span></a></p><p><a href="https://fediscience.org/tags/HeatExchangerNetworkDesign" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HeatExchangerNetworkDesign</span></a><br><a href="https://fediscience.org/tags/HeatExchangerNetworkSynthesis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HeatExchangerNetworkSynthesis</span></a><br><a href="https://fediscience.org/tags/HENS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HENS</span></a><br><a href="https://fediscience.org/tags/ProcessIntegration" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProcessIntegration</span></a><br><a href="https://fediscience.org/tags/GreedyAlgorithm" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GreedyAlgorithm</span></a><br><a href="https://fediscience.org/tags/ProcessSystemsEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProcessSystemsEngineering</span></a><br><a href="https://fediscience.org/tags/Visualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Visualization</span></a><br><a href="https://fediscience.org/tags/JuliaLang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>JuliaLang</span></a><br><a href="https://fediscience.org/tags/Optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Optimization</span></a><br><a href="https://fediscience.org/tags/ProcessOptimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProcessOptimization</span></a><br><a href="https://fediscience.org/tags/blog" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>blog</span></a></p>

New blog post on the inspiration for a new Julia package I am writing.

The package will address the optimization based design of heat exchanger networks for process integration (energy use reduction). The optimization will be based on decision variables defined on the basis of visualisation of hot and cold streams in a process.

The blog post illustrates the concepts using gnuplot src blocks in org mode in Emacs and highlights a nice feature of code reuse in org mode. There is also a small example of the use of Emacs Calc to do some algebra.

The plan is to have a series of blog posts as the code is developed. This is the first in the series.

#HeatExchangerNetworkDesign #HeatExchangerNetworkSynthesis #HENS #Optimization #ProcessOptimization #ProcessIntegration #visualization #Emacs #Calc #EmacsCalc #orgmode #gnuplot #JuliaLang #blog

homepages.ucl.ac.uk/~ucecesf/b

www.homepages.ucl.ac.ukQTHEN: 1. Inspiration from visualization for heat exchanger network design

Dmitry Grinberg joined Elecia( @logicalelegance ) and Chris( @stoneymonster ) to talk about running Linux on small microprocessors (physically small and/or 4-bit).

The transcript( embedded.fm/transcripts/506 ) from the show is also available now!

You're invited to share your favorite quotes.

Thank you to Mouser Electronics for sponsoring us!

Embedded506: How Do I Fit a Whale Into an Apartment Building? — EmbeddedTranscript from 506: How Do I Fit a Whale Into an Apartment Building? with Dmitry Grinberg, Christopher White, and Elecia White.
Continued thread

Some quotes from the text:

"The goal is always to optimize the fulfillment of needs while keeping decisions and data as local as possible."

"Or in complex system lingo: The constraints are slightly different, which might result in the systems sliding into different attractor states."

"We can make use of the ideas of cybernetics if and when they seem useful to solve a problem, but they don't force us to act or think in a given way."

New article published today in the Computers & Chemical Engineering journal: *A multi-agent system for hybrid optimization*. This paper is based on the work presented last year at the European Symposium on Computer Aided Process Engineering (ESCAPE 34) conference.

One key lesson I took away from this work: how easy it is to write efficient and effective multiprocessing code in Julia. And how much fun it is actually. 🙂

#MultiAgentSystem #Agents #Optimization #HybridOptimization #ProcessSystemsEngineering #CAPE #PSE #JuliaLang

doi.org/10.1016/j.compchemeng.