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]]>Mon, 18 May 2026 00:00:00 +0000

For this week’s blog post, we invited the DuBois Area Robotics team to share their journey and how they used MATLAB and Simulink for the 2025 BEST Robotics Championship. MathWorks is immensely proud of their achievements, and we hope you also find their insights useful! With that, I’ll hand it over to them.

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For this week’s blog post, we invited the DuBois Area Robotics team to share their journey and how they used MATLAB and Simulink for the 2025 BEST Robotics Championship. MathWorks is immensely proud of their achievements, and we hope you also find their insights useful! With that, I’ll hand it over to them.

read more >>

]]> Thu, 14 May 2026 00:00:00 +0000

※この投稿は 2026 年 5 月 11 日に The MATLAB Blog へ 投稿されたものの抄訳です。 --

今日のゲストブロガーは Rikin Mehta です。Rikin は MathWorks の Database Toolbox チームのソフトウェア エンジニアです。この投稿では、エージェントがどこで苦労しているかを発見することから、実際に問題を解決するものを作成することまで、MATLAB Agentic Toolkit の ORM スキルを開発した背景にあるストーリーを共有します。

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※この投稿は 2026 年 5 月 11 日に The MATLAB Blog へ 投稿されたものの抄訳です。 --

今日のゲストブロガーは Rikin Mehta です。Rikin は MathWorks の Database Toolbox チームのソフトウェア エンジニアです。この投稿では、エージェントがどこで苦労しているかを発見することから、実際に問題を解決するものを作成することまで、MATLAB Agentic Toolkit の ORM スキルを開発した背景にあるストーリーを共有します。

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]]> Thu, 14 May 2026 00:00:00 +0000

Verification, standards, and the role of executable models in digital engineering Trust Is Proven, Not Assumed In the industries our customers operate in, including aerospace and defense, automotive, industrial automation and machinery, medical devices, and rail, trust is never assumed. It is proven. These industries are built on an uncomfortable truth: humans make mistakes. Systems behave in unexpected ways. Interactions compound risk. That is why certification frameworks and standards such as DO‑178C for aerospace and defense, ISO 26262 for automotive functional safety, and IEC 61508 for industrial and railway systems exist. They are not bureaucratic overhead. They are how organizations enforce discipline, reduce risk, and deliver systems they can stand behind. For background on these standards and how they shape engineering practice, see MathWorks resources for: Aerospace and...

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Verification, standards, and the role of executable models in digital engineering Trust Is Proven, Not Assumed In the industries our customers operate in, including aerospace and defense, automotive, industrial automation and machinery, medical devices, and rail, trust is never assumed. It is proven. These industries are built on an uncomfortable truth: humans make mistakes. Systems behave in unexpected ways. Interactions compound risk. That is why certification frameworks and standards such as DO‑178C for aerospace and defense, ISO 26262 for automotive functional safety, and IEC 61508 for industrial and railway systems exist. They are not bureaucratic overhead. They are how organizations enforce discipline, reduce risk, and deliver systems they can stand behind. For background on these standards and how they shape engineering practice, see MathWorks resources for: Aerospace and...

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]]>Thu, 14 May 2026 00:00:00 +0000

A practical MATLAB walkthrough comparing tracking error and exact exposure approaches.

When you build a factor-based portfolio, the central design choice is how strictly to enforce your factor views. Do you treat target exposures as soft goals or do you impose them as hard constraints that the portfolio must satisfy exactly? That decision can materially change the diversification, concentration, and feasibility of the resulting portfolio.

In this blog post, we use data from the Fama-French Data Library, a widely used academic source, to make the comparison practical in a realistic setting. While the example uses the Fama-French three-factor structure—market (Mkt), size (SMB), and value (HML)—the techniques apply to any multi-factor framework.

We compare two practical optimization approaches using MATLAB®:

Tracking Error Minimization—Targets specific factor exposures...

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A practical MATLAB walkthrough comparing tracking error and exact exposure approaches.

When you build a factor-based portfolio, the central design choice is how strictly to enforce your factor views. Do you treat target exposures as soft goals or do you impose them as hard constraints that the portfolio must satisfy exactly? That decision can materially change the diversification, concentration, and feasibility of the resulting portfolio.

In this blog post, we use data from the Fama-French Data Library, a widely used academic source, to make the comparison practical in a realistic setting. While the example uses the Fama-French three-factor structure—market (Mkt), size (SMB), and value (HML)—the techniques apply to any multi-factor framework.

We compare two practical optimization approaches using MATLAB®:

Tracking Error Minimization—Targets specific factor exposures...

read more >>

]]> Thu, 14 May 2026 00:00:00 +0000

Mike's pick this week is DeepSqueak by Kevin R Coffey.

Introducing Alston's singing mouse

This week I learned that there is a type of mouse that sings, Alston's singing mouse Scotinomys teguina)! Here's a video of the singing from the Live

...

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Mike's pick this week is DeepSqueak by Kevin R Coffey.

Introducing Alston's singing mouse

This week I learned that there is a type of mouse that sings, Alston's singing mouse Scotinomys teguina)! Here's a video of the singing from the Live

...

read more >>

]]> Tue, 12 May 2026 00:00:00 +0000

In modern financial markets, pricing, hedging, and risk decisions all hinge on volatility modelling. Yet volatility is not directly observable. It has to be inferred from the prices of listed vanilla options and represented as a surface that encodes forward-looking market expectations across strikes and maturities. This is where conventional calibration approaches start to fail. The Fragile Art of Modeling Volatility The drift term, which captures an asset’s long-term direction, is rigidly set by no arbitrage conditions. Volatility is not. “Volatility is far more challenging,” explains Dr. Benjamin Joseph, Quantitative Researcher and Lead Core App Developer at genOTC. “Current models rely on simplifying parametric assumptions you wouldn’t necessarily want to impose; they are fragile and unstable. When these assumptions break, your model will struggle to replicate what the market is telling you.” Most institutions rely on parametric families such as SVI or SABR that impose...

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In modern financial markets, pricing, hedging, and risk decisions all hinge on volatility modelling. Yet volatility is not directly observable. It has to be inferred from the prices of listed vanilla options and represented as a surface that encodes forward-looking market expectations across strikes and maturities. This is where conventional calibration approaches start to fail. The Fragile Art of Modeling Volatility The drift term, which captures an asset’s long-term direction, is rigidly set by no arbitrage conditions. Volatility is not. “Volatility is far more challenging,” explains Dr. Benjamin Joseph, Quantitative Researcher and Lead Core App Developer at genOTC. “Current models rely on simplifying parametric assumptions you wouldn’t necessarily want to impose; they are fragile and unstable. When these assumptions break, your model will struggle to replicate what the market is telling you.” Most institutions rely on parametric families such as SVI or SABR that impose...

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Mon, 11 May 2026 00:00:00 +0000

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Thu, 07 May 2026 00:00:00 +0000