MaplePrimes - Maplesoft Blog

Estimating Pi Using Randomness - Buffon's Needle

callumneily — Tue, 24 Mar 2026 17:59:57 Z

Mathematics often feels precise and deterministic. We solve equations, follow logical steps, and do our best to arrive at exact answers. But sometimes, surprisingly, randomness can also lead us to deep mathematical truths. One of the most famous examples of this idea is a problem from the 18th century known as Buffon’s Needle.

Imagine you have a floor made of long wooden planks placed side by side. The seams between the planks form a set of equally spaced parallel lines across the floor. Now, suppose you take a needle and randomly drop it onto the floor. Sometimes the needle lands entirely on one plank. Other times, it crosses one of the seams between planks, as shown below.

Now here is the curious question posed by the French mathematician Georges-Louis Leclerc, Comte de Buffon in the 1700s:

If we repeatedly drop the needle at random, what is the probability that it crosses one of the lines on the floor?

At first glance, this sounds like a simple probability puzzle. But the answer turns out to involve one of the most famous numbers in mathematics: π.

To keep things simple, assume the distance between the parallel lines on the floor is the same as the length of the needle. We can also imagine that all of our needles are thrown onto the same plank, potentially crossing onto the plank above or below. This configuration is equivalent to throwing the needle onto any plank as long as the planks are equally wide; this modification makes the analysis much simpler.

Every time the needle lands, two things determine whether it crosses a line:

The distance x from the center of the needle to the nearest line
The angle θ at which the needle lands with respect to the parallel lines

See a depiction of this below.

To determine the probability of a needle crossing one of these lines, we need to describe what a "random drop" of the needle means mathematically. If the lines are the same length apart as the length of the needle L, then the center of the needle can never be farther than L/2 from the nearest line. Therefore, 0 ≤ x ≤ L/2. Next, we can simplify our domain for θ. The problem is symmetric, so we only need to consider angles between 0 and π/2. Any given half of the needle then has a vertical reach of (L/2)sin(θ).

We will say a needle "crosses" a line precisely when the center lands close enough to a line that one end of the needle can reach across the line. This occurs when x ≤ (L/2)sin(θ).

An important assumption to make is that every pair (x,θ) in the rectangle 0 ≤ x ≤L/2, 0 ≤ θ ≤ π/2 is equally likely. We’re assuming the needle lands with uniform randomness over all vertical positions x and angles θ. This means that the probability of crossing a line is the fraction of this region where the inequalities above hold. That is,

Probability = (area of favourable region) / (area of total region)

The "rectangle" formed by inequalities has a total area of (L/2) * (π/2) = π*L/4. The needle crosses a line exactly when x ≤ (L/2)sin(θ), so for a fixed angle θ, the allowable x values are 0 ≤ x ≤ (L/2)sin(θ). The favourable area is then:

The probability of a needle crossing a line is therefore:

This result leads to a fascinating idea. If the probability of crossing a line is 2/π, we can rearrange the formula to estimate π itself:

π ≈ 2N / C

where:

N = the total number of needle drops
C = the number of times the needle crosses a line

In other words, by performing a simple random experiment and counting how often the needle crosses a line, we can approximate π.

For example, suppose you drop the needle 10,000 times and it crosses a line 6,366 times. Plugging these values into the formula gives

π ≈ (2 × 10,000) / 6,366 ≈ 3.14

With enough trials, the estimate tends to get closer and closer to the true value of π. At the bottom of this post, I attached a Maple worksheet that simulates this phenomenon. Below are results from simulating this result using N = 10, 100 & 1000, respectively. Notice as N increases, our approximation for π tends to become more and more accurate.

Below is a more dynamic simulation from the Maple worksheet to show how the approximation stabilizes as N increases.

What makes Buffon’s Needle so fascinating is the unexpected connection between geometry, probability, and one of mathematics’ most important constants.

π usually appears when dealing with circles (circumference, area, rotation, etc). But in Buffon’s experiment, there are no circles at all. Instead, π emerges from the geometry of all the possible ways a needle can land on a set of parallel lines.

This was one of the earliest examples of what we now call a Monte Carlo method, which is essentially using random experiments to estimate numerical values. Today, similar techniques are used in physics, finance, computer graphics, and machine learning.

One of the best parts of Buffon’s Needle is that you can try it yourself. All you need is:

A toothpick or needle
A piece of paper with a sequence of parallel lines, each a distance of the needle's length apart
A lot of patience

Drop the needle repeatedly (N times), record how many times it crosses a line (C), and compute 2N/C. The more times you repeat the experiment, the closer your estimate will get to π.

After reading about this experiment, I was convinced that mathematics is not only about abstract symbols and formulas. Sometimes, even something as simple as dropping a needle onto the floor can reveal the hidden structure of elements of the universe that we would've otherwise never known were there.

Buffons_Needle_Simulation.mw

Gabriel's Horn

callumneily — Tue, 17 Mar 2026 16:15:31 Z

Gabriel’s Horn is one of the most famous examples in calculus of how infinity can behave in ways that completely defy our intuition.

The horn-shaped object is created from a very simple curve: y = 1/x for x ≥ 1 (pictured below).

Now imagine rotating this curve around the x-axis. The resulting surface stretches infinitely far to the right while becoming thinner and thinner. Visually, it resembles a long trumpet or horn that continuously narrows to a thickness of zero.

At first glance, nothing about this shape seems particularly mysterious. As x grows larger, the radius 1/x becomes smaller and smaller. It seems reasonable that both the volume contained inside the horn and the area of its surface would remain finite (or at least if the volume was finite, then the surface area would also be finite). After all, the horn gets extremely thin very quickly.

Calculus allows us to test that intuition.

To compute the volume of the horn, we use the disk method. Each slice perpendicular to the x-axis forms a circular disk of radius r = 1/x, each with an area of π*r² = π*(1/x²).

The total volume is the sum of an infinite number of these disc areas with thickness dx. As an integral,

V = π ∫₁^∞ (1/x²) dx.

This is a simple integral that converges to a value of 1. We could use the power or rule or our favourite computing software (I used Maple below).

Hence, V = π ∫₁^∞ 1/x² dx = π*1 = π. This means the horn contains only π cubic units of space, even though it extends infinitely far.

Now let’s compute the surface area of the horn. For a surface of revolution, the surface area is

A = 2π ∫₁^∞ y √(1 + (y′)²) dx.

Since y = 1/x, we have y′ = −1/x². Substituting into the formula gives

A = 2π ∫₁^∞ (1/x) √(1 + 1/x⁴) dx.

Software like Maple can easily handle this integral. It tells us the integral diverges to infinity.

However, this is difficult to solve analytically. To understand what happens to this integral, notice that for large x, the square root term is very close to 1, since 1/x⁴ can be approximated as 0 as x grows large. This means the integrand behaves roughly like 1/x (it's actually slightly larger than 1/x). But

∫₁^∞ 1/x dx diverges, and ∫₁^∞ (1/x) √(1 + 1/x⁴) dx > ∫₁^∞ 1/x dx, so ∫₁^∞ (1/x) √(1 + 1/x⁴) dx must also diverge. As a result, the surface area of Gabriel’s Horn is infinite.

This leads to the famous, surprising conclusion:

The horn has finite volume.
The horn has infinite surface area.

In other words, it could be filled with a finite amount of paint, but it would require an infinite amount of paint to coat its inside surface.

Of course, real paint has thickness, so the paradox disappears in the physical world. Eventually, the horn would become thinner than the paint layer itself. But mathematically, the result is perfectly consistent.

The key idea lies in how quickly the function 1/x shrinks. The cross-sectional area of the disks scales like (1/x)² = 1/x², and the integral of 1/x² converges.

But the circumference of each slice scales like 1/x, and the integral of 1/x diverges.

So as the horn extends outward, the added volume decreases quickly enough to sum to a finite value, while the added surface area decreases too slowly and accumulates forever.

Gabriel’s Horn beautifully illustrates one of the central themes of calculus: infinite processes can produce results that feel deeply counterintuitive.

Volume and surface area seem closely related, but can behave in completely different ways when infinite limits are involved. A shape can stretch endlessly yet still contain a finite amount of space.

This strange object reminds me that mathematics isn’t just about calculating numbers, but is also about exploring the strange and fascinating consequences of simple ideas pushed to their limits.

Maple 2026 is Here

Karishma — Wed, 11 Mar 2026 16:26:39 Z

For decades, Maple has been built around one of the world’s most powerful mathematics engines—helping students, educators, engineers, and researchers explore ideas, solve complex problems, and communicate mathematics clearly.

Maple 2026 builds on that foundation with major advances in the math engine, expanding the kinds of problems Maple can solve while improving reliability and performance.

At the same time, Maple 2026 introduces new AI-powered tools that help you work faster—finding commands, generating visualizations, explaining concepts, and helping you explore ideas. The key difference is that these tools sit on top of Maple’s math engine, so the results are grounded in real computation rather than guesswork.

If you’ve been following along with our recent Mathy teaser videos and sneak peek posts, you may already have seen hints of some of these features. Now I’m excited to finally share them in full.

One of the most exciting additions in Maple 2026 is the new AI Assistant.

AI tools are incredibly useful for exploring ideas, writing code, and learning new topics. But when the mathematics becomes more involved, relying on AI alone can be risky. The Maple AI Assistant brings those productivity benefits into Maple while keeping the mathematics grounded in Maple’s trusted computation engine.

You can ask the AI Assistant questions in natural language and have it help you:

find Maple commands or formulas
generate Maple code
create visualizations
explain mathematical concepts
draft examples, worksheets, or reports

Because Maple performs the underlying computations where appropriate, the results are grounded in Maple’s powerful math engine. The AI Assistant becomes a productivity partner that helps you accomplish tasks in Maple faster and more easily, combining the flexibility of AI with mathematics you can trust.

Watch the AI Assistant in action.

Turn Documents into Live Mathematics

Another feature I’m particularly excited about is Document Import.

Many of us have years of mathematical content stored in PDFs, lecture notes, journal articles, slides, or even handwritten pages. Traditionally these documents are static—you can read them, but you can’t interact with the mathematics inside them.

With Maple 2026, that changes.

Document Import allows Maple to convert many document formats—including PDFs, DOCX files, and presentations—into Maple worksheets where the mathematics becomes live and executable.

The image below illustrates the transformation.

On the left (“Before”), scribbled handwritten notes from a Calculus III lecture were saved in a Word document. The notes include hand-drawn sketches, formulas, and written explanations.

After importing the document into Maple (“After”), the mathematical expressions were recognized and converted into live, editable Maple mathematics. The text was preserved, and the hand-drawn sketches were retained as images. The resulting worksheet supports evaluation, editing, and further computation.

Once imported, you can:

evaluate expressions
modify formulas
extend derivations
add visualizations
explore variations of the mathematics

Instead of recreating examples from scratch, you can bring existing material directly into Maple and start exploring.

While the new AI features are exciting, the heart of Maple has always been its mathematics engine—and Maple 2026 delivers significant advances here.

One particularly notable improvement is Maple’s expanded ability to solve linear recurrence equations. Through improvements to the rsolve command and major extensions to the LREtools package, Maple can now solve dramatically more recurrence relations than before, including many third- and fourth-order cases that were previously beyond reach.

In fact, Maple can now fully solve over 94% of the 55,979 entries in the Online Encyclopedia of Integer Sequences (OEIS) that that can be shown to satisfy a linear recurrence relation. These advances reflect ongoing research into linear difference equations and their algorithmic implementation in Maple, continuing Maple’s long tradition of advancing the state of computer algebra.

Beyond recurrence solving, Maple 2026 includes many improvements across its core symbolic and numeric algorithms. Maple’s assumption system has been strengthened to improve reasoning under mathematical assumptions, and enhancements to the simplify, combine, and evalc commands allow Maple to produce more compact and mathematically natural forms for a wider range of expressions.

There are also improvements to Maple’s differential equation solvers, polynomial system solving, and numerical solving routines such as fsolve, along with updates to other foundational parts of the math library used throughout the system.

Taken together, these improvements expand the range of problems Maple can solve and improve the robustness, correctness, and efficiency of the results.

Maple has always offered extensive control over plotting options, but achieving consistent visual styling across multiple plots could require specifying many settings each time.

Maple 2026 introduces Plotting Themes, which allow you to define a plotting style once and apply it across many plots with a single option.

Themes make it easy to maintain consistent visual styles in worksheets, teaching materials, reports, and publications, while still allowing individual plots to override specific options when needed.

The image below shows an example of creating and applying a custom plotting theme.

Maple continues to be widely used in classrooms around the world, and Maple 2026 includes several improvements designed to support teaching and learning.

The Check My Work system has been enhanced so Maple can recognize a wider variety of valid student solution steps and provide more accurate feedback.

Maple 2026 also improves the generation of similar practice problems, making it easier to create variations of a problem while preserving its mathematical structure.

In addition, Maple’s step-by-step solutions have been expanded to support more types of expressions, helping students better understand the reasoning behind the mathematics they’re learning.

Maple 2026 also introduces improvements for developers building advanced applications, along with performance enhancements across the system.

One particularly interesting addition is the new VectorSearch package, which implements a vector database directly inside Maple.

If you’re not familiar with vector databases, one way to think about them is through recommendation systems like Netflix or Spotify. Each movie or song can be represented by a vector containing thousands of numbers describing its characteristics—things like genre, pacing, or mood. When you watch something, the system finds other items whose vectors are closest to it, which is how recommendations are generated.

With the new VectorSearch package, Maple can store thousands (or more) of vectors and efficiently find the ones most similar to a given vector. This makes it easier to build applications involving machine learning, data analysis, and modern AI workflows directly in Maple.

Maple 2026 also delivers significant performance improvements. For example, operations involving quantities with units have been greatly optimized—some computations now run over 90 times faster, making Maple even more efficient for engineering and scientific workflows.

Maple 2026 also expands the benefits available through the Maplesoft Elite Maintenance Program (EMP). The new benefits include access to additional Maplesoft products and services:

Maple Learn, the online environment for teaching and learning mathematics
Maple Calculator Premium, bringing the power of Maple to your phone with full access to features like Solution Steps and Check My Work
Maple MCP, which allows you to connect Maple’s math engine to external AI tools so they can produce mathematical results you can trust

These additions extend Maple beyond the desktop, giving users powerful tools for learning, teaching, and exploring mathematics across web and mobile platforms, as well as through integrations with external AI tools.

This post only scratches the surface of what’s new in Maple 2026. There are many more improvements across the math library, programming tools, and performance.

To learn more about all the new features and enhancements in Maple 2026, visit the What’s New in Maple page on our website.

Announcing Maple Flow 2025.2

Samir Khan — Thu, 12 Feb 2026 17:33:54 Z

We've reached quite a rhythm with Maple Flow - we update frequently, we add lots of improvements and we move fast.

What does this mean for you? It means that the feedback loop between development, the user experience and course correction has a fast time constant.

Without you being loud and vociferous, the feedback loop breaks. So don't be shy - tell us what you want!.

The new 2025.2 update builds on the theme of connectivity with two popular tools - Excel and Python. On top of that, we also have many other features and fixes that you've asked for.

Earlier versions of Maple Flow let you

import and and export Excel data from Excel spreadsheets
run Maple Flow worksheets in Excel with a mouse-driven UI to generate the communication glue

With the 2025.2 update, you can now copy and paste data from Excel into a Flow worksheet.

To be blunt, this is type of cross-application copy-paste behaviour is a no-brainer. It's such a natural workflow.

We've increasignly found that Python is now being used to script the interaction and data flow between different engineering tools. With Maple Flow 2025.2, you can now execute Maple Flow worksheets from a Python script.

From Python, you can change and export any parameters and results defined in the worksheet

This gives me the dopamine hit of watching CPU utilization spike in the Task Manager (hey..I get my kicks where I can)

You can now do your parameter sweeps more quickly by executing the same worksheet in parallel, changing parameters for every run.

This is easy to set up - no special programming is needed.

Print Extents can now be set globally for all sessions, or just for the current session.
Any user-installed fonts used in the worksheet are now respected in the PDF export
Worksheets execute faster
The update includes fixes to many user-reported issues

You can install the Flow 2025.2 update via Help > Check for Updates (or if you're not already in the race, then grab a trial here and take Flow for a spin).

We're not pulling back on this aggresive development velocity, but we need you to point us in the right direction. Let's keep the feedback time constant small!

Meet Mathy

Karishma — Wed, 04 Feb 2026 20:08:04 Z

If one of our posts showed up in your social media feed recently, you may have found yourself staring at a giant maple leaf with feet and thinking, “Wait… who (or what) is that?” you’re not alone.

Yes, that big, cheerful leaf you’ve been seeing is very real.
And yes, they have a name.

Meet Mathy.

We officially introduced Mathy to the world a couple of weeks ago at JMM 2026 in Washington, DC, but their story actually started much earlier.

Mathy was originally created by one of our developers, Marek Krzeminski, a few years ago as a fun internal character. Over time, they quietly became our in-office, local mathscot, popping up as mini 3D-printed Mathys around the office and even as a custom emoji someone created.

Then, sometime last year, someone had what can only be described as a bold idea:

What if we brought Mathy to life?

And just like that, the giant maple leaf went from concept to costume.

Mathy is fun, curious, and a little playful. That’s very intentional. That’s what math should feel like.

We believe math matters. We also believe math should be approachable, joyful, and a place where curiosity is rewarded. Mathy reminds us, and hopefully others, that math doesn’t have to be intimidating. It can be fun, and it can inspire awe.

I’ll be honest. When we decided to bring Mathy to JMM, I was a little nervous. Conferences are busy, serious places. Would people really want to interact with a seven-foot-tall maple leaf?

As it turns out, yes. Very much yes.

Researchers (from postdocs to seasoned academics), educators, and undergraduate and graduate students all stopped, smiled, laughed, and asked for photos. At one point, people were actually lining up to take pictures with Mathy.

Let’s just say: Mathy was a hit.

How tall is Mathy?
About 7 feet. They are hard to miss.

What does Mathy love (besides math)?
Dancing. Very much dancing.
You can see for yourself here: Mathy's got moves!

Does Mathy talk?
You bet they do.

Now that Mathy has officially been introduced to the world, you’ll be seeing them more often on social media, at events, and in a few other fun places we’re cooking up.

So if you spot a giant maple leaf dancing, waving, or talking math, now you know who they are.

If you spot Mathy, don’t be shy, say hi.

Contest problem: area under quadratic inside a square

callumneily — Tue, 03 Feb 2026 18:07:15 Z

Many problems in mathematics are easy to define and conceptualize, but take a bit of deeper thinking to actually solve. Check out the Olympiad-style question (from this link) below:

Former Maplesoft co-op student Callum Laverance decided to make a document in Maple Learn to de-bunk this innocent-looking problem and used the powerful tools within Maple Learn to show step-by-step how to think of this problem. The first step, I recommend, would be to play around with possible values of a and b for inspiration. See how I did this below:

Based on the snippet above, we might guess that a = 0.5 and b = 1.9. The next step is to think of some equations that may be useful to help us actually solve for these values. Since the square has a side length of 4, we know its area must be 4² = 16. Therefore, the Yellow, Green and Red areas must add exactly to 16. That is,

With a bit of calculus and Maple Learn's context panel, we can integrate the function f(x) = ax² from x = -2 to x = 2 and set it equal to this value of 8/3. This allows us to solve for the value of a.

We see that a = 1/2. Since the area of the Red section must be three times that of the Yellow (which we determined above to be 8/3), we get Red = (8/3)*3 = 8.

The last step is to find the value of b. In the figure below, we know that the line y = 4 and the curve y = bx² intersect when bx² = 4 (i.e. when x = ± 2/sqrt(b)).

Since we know the area of the red section is 8 square units, that must be the difference between the entire area underneath the horiztonal line at y = 4 and the curve y = bx² on the interval [-2/sqrt(b), 2/sqrt(b)]. We can then write the area of the Red section as an integral in terms of b, then solve for the value of b, since we know the Red area is equal to 8.

Voila! Setting a = 1/2 and b = 16/9 ≈ 1.8 guarantees that the ratio of Yellow to Green to Red area within the square is 1:2:3, respectively. Note this is quite close to our original guess of a = 0.5 and b = 1.9. With a bit of algebra and solving a couple of integrals, we were able to solve a mathematics Olympiad problem!