Cleve Moler博士が2026年5月20日、86歳で逝去されました。心よりご冥福をお祈りいたします。彼はFortranが科学技術計算の絶対的標準だった1970年代後半、学生たちがLINPACKやEISPACKを「プログラミングせずに」簡単に使えるようにという思いから、MATLABの原型をFortranで作りました。それが後に「計算の民主化」を象徴するツールへと育っていきます。1984年、Jack Littleらと共にMATLABをC言語で全面的に書き直し、MathWorksを設立。PC時代に対応した商用版をリリースしたことで、世界中の研究者・エンジニアが行列演算や数値解析を直感的に扱える環境が広がりました。以降、ツールボックス、Simulink、グラフィックス、さらには現代のNumPy/SciPyやJuliaにも影響を与えるほどの巨大なエコシステムへと発展。プログラミングの専門家でなくても科学を加速できる「道具」を提供し続けた功績は計り知れません。Cleve Moler博士の遺した哲学——「コードを書く手段ではなく、発見を加速させる道具」——は今も生き続けています。本当にありがとうございました。安らかにお眠りください。#CleveMoler #MATLAB #数値計算 #科学技術計算 #NumericalComputing

📣 Nelson's next release brings tilelayout, nexttile, tilenum & tilerowcol! Full tiled figure layout support, now open-source. 🚀 #OpenSource #NumericalComputing #DataVisualization #stem #Software #Engineering #HPC #DataScience #OSS #FOSS #GNUoctave #scilab #Matlab #NelsonLang

MultiFloats.jl v3.0 released: MultiFloats.jl is the world's fastest library for extended-precision floating-point arithmetic with 128–256 bits (30–60 decimal digits).MultiFloats.jl v3.0 introduces new arithmetic algorithms that are simultaneously faster and more accurate than MultiFloats.jl v2.0. github.com/dzhang314/MultiFl… #JuliaLang #MultiFloats #HighPrecision #NumericalComputing #ScientificComputing #HighPerformanceComputing #HPC #OpenSource #FloatingPoint #Mathematics #ComputationalScience #JuliaCommunity

【Drop the “global” in global sums. 31× faster with a strict error bound】 We released a one-shot demo that replaces an expensive global / long-range sum with a finite FFT-based convolution. ・Runtime: 12.84s → 0.41s (31.3× speedup) ・Accuracy: |Δresult| ≤ ε (PASS guaranteed) ・Reproducibility: params hash for full verification Repo (with GIF): github.com/GhostDriftTheory/… #FFT #NumericalComputing #HPC #Algorithms

💪High-level scripting doesn’t have to mean slow execution. medium.com/@DolphinDB_Inc/ma… In this article, we walk through how JIT compilation bridges flexibility and performance—showing how compute-intensive scripts can be compiled into efficient machine code at runtime. From infinite series and numerical integration to real-world financial modeling, the examples highlight where JIT truly shines. If you work with iterative, performance-sensitive workloads, JIT is worth a closer look. Learn more about us: dolphindb.com/ #DolphinDB #JIT #HighPerformanceComputing #RuntimeOptimization #LowLatency #NumericalComputing #QuantitativeFinance #DeveloperTools #DataEngineering

Day 30 #DataScience!🚀 Unpacked NumPy's core features. It's not just fast—arrays use less memory than Python lists! 🧠 Explored np.ones() & np.zeros() today. A game-changer for performance!💡 Next up: advanced NumPy functions!🐍 #Python #Numpy #NumericalComputing #LearningJourney

Day 29 #DataScience! 🚀 Digging deeper into NumPy today. Mastered arrays and random.randint, seeing firsthand the speed & efficiency advantage over Python lists. A game-changer! 💡 Next up: More advanced NumPy functions! 🐍 #Python #Numpy #NumericalComputing #LearningJourney

Can MATLAB understand Python's ssssnakey syntax or is it hisssstory? Source: devhubby.com/thread/how-to-d… #NumericalComputing #CodingLife #DeepLearning #DataScience

🚀 𝗜𝘁'𝘀 𝗟𝗶𝘃𝗲! 𝗡𝘂𝗺𝗲𝗿𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆 𝗶𝘀 𝗡𝗼𝘄 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲! 🧮✨ The wait is over! Our 𝗡𝘂𝗺𝗲𝗿𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆 skill track is officially live on 𝗗𝗮𝘁𝗮𝗪𝗮𝗿𝘀. 🎉 If you're looking to 𝗺𝗮𝘀𝘁𝗲𝗿 𝗮𝗿𝗿𝗮𝘆 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻, 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀, 𝗮𝗻𝗱 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗻𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀, this track is for you! Whether you're diving into data analysis, machine learning, or scientific computing, NumPy is the foundation that powers it all. 🔍 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗲𝘅𝗽𝗹𝗼𝗿𝗲: ✅ Array fundamentals and operations ✅ Indexing and slicing techniques ✅ Linear algebra and statistical functions ✅ Data cleaning and visualization ✅ Applications in audio/image processing But that’s not all! 📢 𝗔𝗹𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗶𝘀 𝘁𝗿𝗮𝗰𝗸 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗽𝘂𝗯𝗹𝗶𝗰𝗹𝘆 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗔𝗽𝗿𝗶𝗹 1, 2025! This means you'll be able to access, review, and experiment with real-world NumPy-based projects to deepen your learning. 📅 𝗚𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝘁𝗼𝗱𝗮𝘆 𝗮𝗻𝗱 𝘁𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝘀𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗹𝗲𝘃𝗲𝗹! 🚀 #NumPy #DataScience #Python #MachineLearning #DataAnalytics #DataWars #NumericalComputing

🚀 𝗖𝗼𝗺𝗶𝗻𝗴 𝗦𝗼𝗼𝗻: 𝗡𝘂𝗺𝗲𝗿𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆! 🔢✨ Are you ready to take your data science skills to the next level? 𝗢𝗻 𝗠𝗮𝗿𝗰𝗵 20, 2025, we’re launching a brand-new 𝗡𝘂𝗺𝗲𝗿𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆 skill track on 𝗗𝗮𝘁𝗮𝗪𝗮𝗿𝘀! This track is designed to help you build a strong foundation in 𝗮𝗿𝗿𝗮𝘆 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻, 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀, 𝗮𝗻𝗱 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗻𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀—all essential for data analysis, machine learning, and scientific computing. 𝗪𝗵𝗮𝘁’𝘀 𝗜𝗻𝘀𝗶𝗱𝗲? 🔥 📌 Array Fundamentals and Operations 📌 Indexing and Slicing Techniques 📌 Linear Algebra Operations 📌 Statistical Functions 📌 Data Cleaning with NumPy 📌 Visualization with NumPy 📌 Applications of Audio/Image Processing 💡 𝗪𝗵𝘆 𝗟𝗲𝗮𝗿𝗻 𝗡𝘂𝗺𝗣𝘆? NumPy is THE foundation of Python’s scientific computing stack, powering everything from data analysis to AI. This skill track will help you 𝘄𝗿𝗶𝘁𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗰𝗼𝗱𝗲, 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗹𝗮𝗿𝗴𝗲 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀, 𝗮𝗻𝗱 𝘀𝗽𝗲𝗲𝗱 𝘂𝗽 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗿𝗼! 🚀 👉 𝗠𝗮𝗿𝗸 𝘆𝗼𝘂𝗿 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿𝘀 – 𝗠𝗮𝗿𝗰𝗵 20, 2025! Stay tuned for updates and get ready to dive deep into NumPy! #DataScience #Python #NumPy #MachineLearning #DataAnalytics #ComingSoon #DataWars #NumericalComputing

How can compiler optimizations enhance numerical methods? Explore Symbolic-Numerics and its impact on nonlinear solvers in Julia for better performance & efficiency. Learn more! stochasticlifestyle.com/symb… #JuliaLang #NumericalComputing #Optimization #SymbolicMath #sciml #MachineLearning

Stepping into the 2024🚶‍♂️💻 #NumericalComputing #MATLAB #Simulation

Equinox: Elevating Numerical Methods for Data Science and Machine Learning #advancedcapabilities #AI #artificialintelligence #attentionmechanisms #checkpointedwhileloops #datascience #Equinox #JAXlibrary #llm #machinelearning #numericalcomputing multiplatform.ai/equinox-ele…

Low-level numerical issues, like round-off errors and numerical precision problems, can significantly impact various computing areas such as weather forecasting, machine learning, and statistical estimation. These problems arise due to limitations in representing magnitude and precision of numbers in computers. Being aware of these challenges is crucial in achieving accurate and reliable results. #NumericalComputing

🧵 Thread 8/10: 7️⃣ Julia 📈 As data science and scientific computing expand, Julia's popularity soars. It's designed for high-performance numerical analysis. Level up your data science skills with Julia! 📊🔬 #JuliaLang #DataScience #NumericalComputing

It's a numbers game and @lehighmem Prof. Hannah Dailey's class is winning it. Her Numerical Methods class uses fun card games and puzzles to flex your brain. ♥️ ♠️ Watch the whole video: bit.ly/3jIYKUL #numericalcomputing #numericalmethods #numerics #codingchallenge

The Right Way to Compare Floats in Python #Python #Programming #NumericalComputing davidamos.dev/the-right-way-…

With Julia Symbolics, how can I do a mathematical summation? stackoverflow.com/q/71140858… #julia #coding #programming #developer #julialang #juliaset #datascience #scientificprogramming #numericalcomputing

You can't spell SciPy without spicy. #python #scientificcomputing #numericalcomputing

Constructor takes Integer but not String stackoverflow.com/q/69980738… #julia #coding #programming #developer #julialang #juliaset #datascience #scientificprogramming #numericalcomputing