Let’s dive in! 🪂

1) 🤖 Vibe-coding AI agents to solve optimization problems

I’ve run a fast test.

I wanted to know how to embed an optimization solver into an AI Agent workflow.

So I used Claude Code (and tested Codex CLI, though not that much).

What it does:

→ Goes to LinkedIn

→ Opens the Queens game

→ Reads the board

→ Solves it

→ And clicks to place the Queens automatically

In its first try, it took 5 min 48 seconds to solve it, and after improving the workflow a bit, 1 min 02 seconds.

It’s not perfect yet, but it shows something powerful: how you can plug an optimization engine inside an LLM Agent.

The result: intuition from AI, rigor from OR.

2) 🎓 From academic to industrial settings

Moving from university to an industry environment can be difficult.

You don’t know how to translate business problems into math/algorithms, ship solutions, or communicate with stakeholders.

But next Wednesday 22nd October you’ll be one step closer to understanding that shift thanks to this Nextmv event:

3) 🎯 Suboptimal solutions are sometimes the best

A model is a simplification of reality, so it will not include all the details (by definition).

Also, an optimization model is an investment, so use it wisely to help you better understand the reality of your problem.

That’s why Jeff Cam reminded us that optimal solutions are not always the best ones.

Read more here:

Feasible

📈 #25 OR in action: why suboptimal solutions are sometimes the best

I've been dealing with a terrible spring allergy for a the last couple of days, and it's only the end of January…

Read more

2 years ago · Borja Menéndez

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Get the full version

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Collaborate now

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) ↔️ Comparing OR to ML

Machine Learning is -without question- far more popular than OR.

Yet the two fields are closer than they seem.

That closeness often tempts us to compare them, usually out of a desire for OR to gain the same visibility and recognition it deserves. I’ve done it myself. And just recently, Laura Albert and co-authors captured this feeling perfectly in their paper “Making OR more accessible: insights from the rise of ML”.

Having a role model like ML isn’t a bad thing. In fact, it can be incredibly useful, as long as we approach it with intention.

Instead of envy, we can use it as a mirror: to identify what’s holding OR back and to tackle those issues one by one, with a concrete plan.

2) 🗣️ OR in the age of ML

Even saying the name of our field can be a problem.

Operations Research.

It rarely speaks for itself. You end up explaining what it means: decisions, optimization, algorithms, complexity… and by the time you finish, the person you’re talking to is already thinking of something else.

And this isn’t just my experience. I’ve seen it happen countless times to colleagues too:

(click in the image to go to the post)

3) ⁉️ Should we rebrand the field?

The naming debate in OR isn’t just semantics, it exposes deeper challenges at the intersection of branding, professional identity, and market communication.

The appearance of new labels shows how the field constantly reframes itself to sound more modern and aligned with tech trends. But frequent name changes can also create confusion, rebranding fatigue, and even signal instability to outsiders. So the real question is: should we change the name at all?

But that’s only the surface.

Beneath it lies a much bigger problem: the lack of recognition that keeps OR from being understood, adopted, and valued.

What we face isn’t just a naming issue, it’s a marketing one.

And that’s where the next challenge begins:

how do we communicate OR in a way the world actually understands?

Read about the marketing challenges here:

Feasible

📈 #54 Operations Research reimagined (Part IV): marketing

Imagine walking into a room and introducing yourself as an "Operations Researcher…

Read more

a year ago · 3 likes · Borja Menéndez

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Get the full version

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Collaborate now

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) ⌛ Predicting runtimes

Yesterday I was debating with my wife about predicting running times in optimization problems.

(She’s a physicist working as Data Scientist, meaning she’s a very analytical person + know about Machine Learning algorithms, and has more optimization knowledge than the average person.)

Anyway.

I argued it’s a super difficult task, if not impossible, due to:

• the structure of the problem,

• parameterization,

• and even what you define as “solved”

…make it a moving target.

2) 🤔 Others on this

Curious as I am, I went digging and found an interesting r/optimization thread:

(click on the image below to go to the source)

3) 🏎️ The true driver of solver speed

Here’s a question I love to ask:

1. Would you rather solve a MIP with today’s solvers on a 1991 machine?

2. Or with 1991 solvers on today’s machine?

In other words: what matters more, hardware or software?

Here’s why I think software is the true driver:

Feasible

📈 #47 Why software is the true driver behind faster solvers

Two weeks ago I told you why NVIDIA’s solvers might be the future…

Read more

2 years ago · 2 likes · Borja Menéndez

A short overview of the most impactful surveys around optimization

Next Monday, I’ll share my take on two recent optimization surveys, including the State of Mathematical Optimization 2025.

I’ll cover 📌 what’s in them for you as an OR practitioner and 🧭 strategic implications.

If you care about how our work is evolving, you’ll want to read that one.

See you Monday!

PS: take a look at my first impressions to the Mathematical Optimization report.

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Get the full version

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Collaborate now

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 👑 The programming language of OR

Machine learning catapulted Python into default status.

There’s a huge ecosystem of libraries that make prototyping models and deploy production code effortless. When ML teams standardized on Python, the rest of the analytics world took notice.

Operations Research followed naturally. OR projects increasingly need data-driven pipelines and seamless integration with company data stacks. Python offers all of that in one place: mature OR toolkits, direct access to ML frameworks, and production-ready web frameworks for APIs.

The result is a faster prototyping, easier collaboration with data scientists, and a single language from data ingestion to optimization and deployment.

Python is the bridge between classical optimization and the ML workflows shaping modern decision-making.

2) 🙊 Which one are you using?

There’s one clear winner, but I’d love to hear your thoughts on this:

(click on the image to go to the post)

3) 🥇 OR-Tools wins again

Last Monday, the MiniZinc challenge announced its winners in different categories.

And without any surprise, the CP-SAT solver from Google OR-Tools won again:

It’s a powerful solver.

I’ve been using it for more than 3 years now, and I had a couple of things to say about it, especially if you’re looking for good documentation.

You can read the post here:

Feasible

📈 #76 Optimizing with Google OR-Tools: strategies and insights from three years of real-world experience

It was nearly the summer of 2020…

Read more

a year ago · 4 likes · Borja Menéndez

Are you the owner of your results?

That’s the same as asking if you’re the owner of the data that feeds your models/algorithms.

Because it’s common that users look at the plan and say “yes, this works” or they dismiss it outright.

And often, the difference between trust and rejection doesn’t lie in the math. It lies in the data.

Next Monday, I’ll share my thoughts on data ownership as an OR Engineer.

I’ll cover:

🔑 Why you must own the inputs
🧭 What ownership really means
🛠️ How to put it into practice

If you are using real-life data to feed your models, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Get the full version

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Collaborate now

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🌊 Good OR vibes

Having the right instructors at university can shape your career.

I know it first-hand as I started my OR journey because of that.

But when those instructors bring passion and excitement, that’s even more powerful. Recently, I’ve seen great enthusiasm towards OR in academic environments:

• Michael Watson is excited to teach Supply Chain Modeling.

• Jesper Larsen sees strong interest in OR from his students.

• Laura Albert shares her energy and excitement à la Michael Watson.

It’s inspiring to see this momentum for Operations Research, optimization, and decision-making, and I hope those good vibes continue like that 🤞🏻

2) 🚚 A CVRP challenge

The CVRPLIB team is launching a 30-day global competition to push the boundaries of the Capacitated Vehicle Routing Problem (CVRP).

What’s new?

• 100 XL benchmark instances: diversified and realistic, with 1,000 to 10,000 customers.

• A real-time leaderboard tracking both per-instance and overall progress.

• Certificates for the overall winner and for teams discovering new Best Known Solutions.

Key dates:

• 📅 Jan 12, 2026 → Competition starts (official XL instances released)

• 📅 Feb 11, 2026 → Competition ends

This is a rare chance to showcase classical algorithms, ML-based methods, or hybrid optimization approaches on one of the toughest CVRPs ever released.

Learn more and register

3) 🍕 VRP transforming logistics

Delivering pizzas around the city can be daunting.

Not only because of the competition with other pizzerias, but also because you can pick up power-ups to improve your speed or time.

Oh no, that was one of my favorite videogames back in the 90s, Radikal Bikers.

Anyway, it’s a complex problem as you need to take into account a number of factors like:

• The location of customers

• The capacity of vehicles

• The size of orders

• Traffic conditions

• Time constraints

The goal of the VRP is to find a set of routes that minimizes the total cost or time of the delivery operation, while satisfying all of the constraints.

But how can you solve it easily? And what applications of it are in the real world?

Read more here:

Feasible

📈 #12 From Pizzerias to Paramedics: How VRP is Transforming Modern Logistics

Radikal Bikers, a fast-paced motorcycle popular racing game from the late 90s, was a favorite of mine…

Read more

2 years ago · 2 likes · Borja Menéndez

Common sources of distrust in OR projects

Next Monday, I’ll share a crucial topic for Operations Research projects: trust.

Or, better said, sources of distrust.

I’ll cover:

🔑 Why trust is all you need
⚠️ Common sources of distrust
🌱 How to overcome them

If you’ve ever felt a lack of confidence in your optimization project or team, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Get the full version

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Collaborate now

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🎒 Going back to school

September is traditionally the month of going back to school…

And here I am! :-)

One of the first things I did was to upgrade the Notion database with Operations Research resources adding an entire new section: use cases.

They’re useful to find inspiration from similar problems, industries, or tools.

Here you can find my announcement.

I’d also like to take advantage of this publication to say a big THANK YOU if you subscribed during August (considering I didn’t write any. single. post).

Literally, dozens of you subscribed during this past month:

Most probably, you did it because I appeared in one of my favorite podcasts.

And speaking about that…

2) 📻 I was featured in The Decision Intelligence Lab podcast

I didn’t write any single word here.

Yet I appeared in one of my favorite podcasts: The Decision Intelligence Lab podcast.

Vijay, Mike and I talked about…

• Not developing for your users.

• The importance of testing your optimization models.

• And where to start when automating decisions at any business.

(among other things)

I encourage you to subscribe to the podcast here:

And why did I appear there? Well, Vijay found my ideas around the Product OR Engineer role powerful 👇🏻

3) 💭 Product OR Engineers

Algorithms, mathematics, chasing optimal solutions…

That’s what academia teaches us.

But that’s a small part of our work to deliver value to the business. What if your focus is on…

• 🎯 Having a business impact mindset.

• 🔥 Owning problems, not just models.

• ⚖️ Balance optimality with usability.

• 🧑‍🤝‍🧑 Build with users, not for them.

All of that, together with writing good software, is what you need to succeed in more integrated roles within organizations.

This shift reflects the transformation in software development, where engineers are taking on responsibilities traditionally held by product managers.

Continue reading here:

Feasible

📈 #70 Are Product OR Engineers the future of Operations Research?

I saw this many more times than I wanted (and I bet you did too…

Read more

a year ago · 1 like · 2 comments · Borja Menéndez

A proof of the Product OR Engineer role

Next Monday, I’ll share something I saw during August: a role that’s speaking directly to those who want to make a real impact.

It’s important because I talked about the Product OR Engineer role back in April, and now it’s materializing into job offers.

I’ll dissect the job offer for you so that you’re able to identify and match its requirements with the ideal candidate.

If you want to see the role of the future, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Get the full version

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Collaborate now

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) ⏳ Understanding the present

George Orwell once said in his brilliant 1984 novel that:

Those who control the present, control the past and those who control the past control the future.

I’m tweaking it a little bit to say that:

Those who understand the past and understand the present will understand the future.

That’s why I’m putting a lot of effort on understanding the present of Operations Research as a field. This is done by posts and polls on LinkedIn, but also by writing this newsletter. This is what gives me time to think and act.

But I feel I lack some part of the history: the past.

I mean, of course anyone can understand how OR was conceived, the algorithms built for decades, and the big voices in the field.

I’m talking about the real-world problems tackled in the 70s, 80s, and 90s that shaped the field as it is today. Those war-rooms with big and ambitious projects that wanted to deliver value to the business in a context that is completely different than today’s.

No cloud, no big databases, no compute power, no memory. Just brains, models, and a lot of whiteboards.

If you worked on one of those projects or know someone who did, I’d love to hear from you. Let’s preserve and learn from that legacy.

And if you want to participate in a poll asking for your years of experience, it’s here (who will win the battle of experience?):

2) ↘️ Local minima in quantum systems

Some weeks ago I found this paper from Google.

It suggests that nature itself -when systems spontaneously cool- may be implicitly solving quantum-hard problems by finding local minima.

(…) We prove that a quantum computer can always find a local minimum efficiently using a thermal gradient descent algorithm that mimics natural cooling processes.

To establish the classical hardness of finding local minima, we construct a family of two-dimensional Hamiltonians such that any problem solvable by polynomial-time quantum algorithms can be reduced to finding local minima of these Hamiltonians.

Therefore, cooling systems to local minima is universal for quantum computation and, assuming that quantum computation is more powerful than classical computation, finding local minima is classically hard but quantumly easy.

Future directions highlighted include applying the algorithm to physically relevant Hamiltonians, exploring quantum advantage in classical optimization, and implementing the approach on near-term quantum devices, laying a foundation for practical quantum optimization methods.

The link to the paper, here.

3) 🎯 Why suboptimal solutions are sometimes the best

As models are a simplification of reality, a suboptimal solution may be better in practice.

Maybe having a super detailed model may make it impossible to solve in a reasonable time?

Maybe many details will not be the ones that tip the final decision?

Maybe we can start small and scaling the problem as needed?

Those are questions that Amazon knows how to answer, as well as professors in the space.

If you want to read more, here’s the full article:

Feasible

📈 #25 OR in action: why suboptimal solutions are sometimes the best

I've been dealing with a terrible spring allergy for a the last couple of days, and it's only the end of January…

Read more

2 years ago · Borja Menéndez

Tractability is all you need

We’re in the final stage of Where did the time go? series of posts.

Remember this series of posts cover different aspects of tractability issues in optimization problems.

Throughout the series, we’ve discussed about benchmarking, large-scale optimization problems, symmetries, algorithms and parallelization, and the use of hardware.

Now we need to close the series with some not-so-random thoughts, so next Monday we’ll cover:

🪨 Understanding is the hard part
⚙️ Tractability drives our careers
🛠️ …And the way we work

If you want to understand the whole picture and think about tractability as a strategy for you to grow, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🤖 Who’s winning the AI war?

CEOs and developers all over the world are confronted in an AI war.

CEOs threaten to fire developers as AI will replace them because it codes faster and reliably good, so they don’t need developers anymore (or at least, not that many).

However, developers see no real value in using AI and a recent study seems to confirm it slows down developers’ experience.

I don’t know if AI will replace us or not, but…

I think it’s just a matter of time that almost everyone embraces AI for coding.

That’s what I argued in this LinkedIn post. Curious if your experience is similar.

Is your experience similar to mine?

2) 🏆 An OR challenge

Are you looking for an optimization challenge?

The 5th edition of the Annual Edinburgh Undergraduate OR Challenge is coming!

It seeks innovative solutions to a problem arising from the energy transition.

(click in the picture below to know more)

3) 🧑🏻‍💻 The power of LLMs in OR

When using LLMs in OR, I see four trends in the field (apart from pure coding or translating a business problem into a math model):

1. Using LLMs for solving optimization problems by manipulating the natural language.

2. Using LLMs for building algorithms that solve optimization problems.

3. Using LLMs for transforming problem descriptions into optimization models.

4. Training LLMs for optimization modeling.

If we step back a bit, everything’s related to the question of:

Can we automate maths?

If you want to read more, here’s the article:

Feasible

📈 #69 Latest news in Operations Research (LLMs edition)

Last Thursday I realized something…

Read more

a year ago · 1 like · 6 comments · Borja Menéndez

Adding more hardware will improve your solutions?

I’m continuing the Where did the time go? series of posts this next Monday.

Remember this series of posts cover different aspects of tractability issues in optimization problems.

The last post we discussed why we’ve been stuck with CPUs. It was the first part of the two-part articles focused on hardware. I needed to split them as they were much longer than expected!

This time, we’ll cover:

🏎️ What faster hardware is on the shelf?
🎯 Extra computational power: what can we still do?

If you want to understand how modern hardware fits into your optimization problems, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🔀 Optimization or optimisation?

As you may already know, I tend to use optimization.

In fact, I never use optimisation.

It’s like I feel that Team Z resonates more. But I’ve seen it written both ways and some people are also in Team S, so I wanted to know how many of us are in the correct path of OR history 😉

How do you spell it?

(click in the image to go to the poll)

2) 🤹🏻 Multi-Objective learning to rank

Amazon has recently released MO-LightGBM: A Library for Multi-objective Learning to Rank with LightGBM.

It’s the first open-source framework that brings multi-objective optimization capabilities into LightGBM’s gradient-boosted decision trees, enabling you to optimize ranking models against multiple criteria in one go.

Why would you care?

There are some applications here, but as I’m focused lately in the hybridization of Machine Learning and Operations Research, I’d say i) warm-start solvers by predicting good initial solutions (à la GNN as we saw the other day), ii) predict good branching decisions for MIP solvers (if you’re building one or creating your own heuristics), iii) bias neighborhood selection toward promising regions of the solution space (for metaheuristic guidance).

If you want to read the paper, here you have it.

3) ✂️ Minimum cut

I love when big companies succeed in improving the way we solve problems or give you new tools for that.

That’s why every time I see Google, Amazon, NVIDIA and the like propose new methods, I actively promote them.

Some months ago, Google proposed a new algorithm to solve the Minimum Cut Problem with a 3-step process:

1. Cut-preserving graph sparsification

2. Connect the Minimum Cut Problem with low-conductance cuts

3. Partition the graph into well-connected clusters

Research has been conducted on this problem for more than 7 decades, and it is now when we have a very efficient and generalist algorithm to solve large-scale problems.

Read more here:

Feasible

📈 #35 Minimum Google Cut

The other day, I read on the Google Research blog that they had made significant advances in solving a typical optimization problem…

Read more

2 years ago · 1 like · Borja Menéndez

Adding more hardware will improve your solutions?

I’m continuing the Where did the time go? series of posts this next Monday.

Remember this series of posts cover different aspects of tractability issues in optimization problems.

This time, we’ll cover:

🤔 Why we’ve been stuck with CPUs
🏎️ What faster hardware is on the shelf?
🎯 Extra computational power: what can we still do?

If you want to understand how modern hardware fits into your optimization problems, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🎉 Want to be featured here?

I’m celebrating I already posted 100 articles in Feasible!

So I'm creating a special celebration issue featuring 100 submissions, a community showcase of cool work in our field: companies, side projects, conferences, courses, research, anything pushing optimization forward..

I call it 100x100 because I’m super creative with names 😅

But what’s in it for you? Each feature gets a short blurb + link so folks can find you later.

This is how it works: you submit your project ➡️ I feature it ➡️ you share the issue when it goes live. That’s it.

Drop your submission

Deadline is tomorrow afternoon!

2) 🏖️ RL Summer School

I started feeling interest in Artificial Intelligence the first time I saw Reinforcement Learning in action.

It was in a Coursera course from the University of Alberta (great course btw), and then when I focused on RL during my Saturdays.AI course (great experience too).

The thing is I saw a link with the optimization space.

Regardless of the techniques, you want to maximize a cumulative reward function, so it touched my heart.

If you want to enter the field, this RL Summer School in Barcelona two years ago has all its content for free.

Enjoy!

3) 🤖 An interview to…

ChatGPT!

Time goes by so fast… Remember the early days of ChatGPT?

I shared it with everyone, and being in one of my cousin’s house we were playing with it, making it saying silly things.

We couldn’t imagine the level of intelligence it currently has.

And I even run a experiment here in Feasible: I interviewed ChatGPT to see its own understanding of the Operations Research field.

It was back in December 2022, right before I stopped writing for several months in a row.

(but here we are now!)

If you want to read what ChatGPT had to say about Operations Research back then…

Feasible

#6 📈 I did an interview with ChatGPT about optimization... And yes, it's amazing

Despite the short time it has been active, I don't even need to introduce ChatGPT... 🤖 But just in case: it is a chat system based on an Artificial Intelligence model developed by OpenAI…

Read more

3 years ago · Borja Menéndez

Algorithms and parallelization: why some are more suitable than others?

I’m continuing the Where did the time go? series of posts this next Monday.

This time, we’ll cover:

🚫 Why some algorithms won’t thread
🫶🏻 Algorithms that love extra cores
🛠 My own experience parallelizing metaheuristics

If you want to understand how to get the most out of your machine, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) ⚔️ This shouldn’t be a competition

The recent FICO post showing Xpress edging out DeepMind’s AlphaEvolve on several open maths benchmarks is impressive in its own right.

It also reminds us we’re not really watching a head-to-head ‘battle’, but two different paradigms at work.

AlphaEvolve is an end-to-end AI agent that evolves code with almost no human guidance, while Xpress is a purpose-built global-optimization engine that shines once an analyst has translated the real-world question into a mathematical model.

In practice, these approaches are complementary: let the solver deliver certifiably optimal answers when you can formalize the problem, and let AI heuristics explore the unknown when you can’t, or when you want to minimize human time in the loop.

Seeing both push the frontier is good news for everyone in Operations Research.

2) 💍 ML + OR

The marriage between Machine Learning and Mathematical Optimization is here to stay.

There are multiple proofs of that, and one of them is this one about AI4OPT, where Junyang Cai, a PhD student. He says:

I develop data-driven strategies that guide the solver’s internal decision-making. By learning from historical problem data and solver behavior, our machine learning models can predict effective branching strategies and other key actions. This leads to a more informed and efficient search process, reducing overall solution times.

Read the full email by clicking in the image (and consider subscribing to AI4OPT newsletter!):

3) 🔮 7 ways AI will reshape OR

I’ve been thinking a lot about how fast things are moving lately. Agentic AI, LLMs writing code, solvers inside chatbots... It’s tempting to believe that everything old is about to become obsolete.

But here’s the thing: some ideas age better than others.

In February, I published my predictions for how AI will transform Operations Research. Looking back, none of them feel outdated. In fact, the world seems to be moving right toward them.

So if you missed it, here’s your chance to catch up:

Feasible

📈 #62 Predictions about OR in the AI era

The other day, I had an interesting conversation with a friend…

Read more

a year ago · 5 comments · Borja Menéndez

Deep Learning to the rescue of Linear Programming

Almost two months ago, I introduced you to how to integrate Deep Learning into OR pipelines.

One thing I said back then was:

But if I like one approach, it's using Graph Neural Networks for optimization problems. They have a lot of potential because we can model many optimization problems as graphs.

(spoiler alert: in a few weeks, we'll explore that option here in Feasible)

Now the time has come, and I’m pretty happy to say it’s a collaborative article!

We’ll cover:

🔌 Understanding the power grid and its optimization problems
🕸️ The challenge of Linear Programming and how Graph Neural Networks help
⚡ Performance gains in real-world problems

If you want to understand how to use GNNs to warmstart Linear Programming problems, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🌱 To be more creative…

Apply the 4R framework that I read the other day.

One of those Rs is…

Restrict: To create something, it’s often much better to think ‘inside a (small) box’ than to go on about ‘thinking outside the box.’ Although it may sound counterintuitive, constraints are a creator’s friends.

This is also true for businesses and optimization problems.

In logistics, what you CAN’T spend is often more important than what you CAN.

• No budget cap? You’ll never weigh ROI.

• No driver constraints? You’ll never optimize routes.

• No order-mix limit? You’ll never cut warehouse time.

Constraints create optimization problems, what businesses care for, and make us more creative.

(and if you want to know what the other Rs mean, just reply to this email or leave a comment here)

2) 🖼️ Algorithms, visually explained

One thing I want to improve is how to visualize ideas.

It’s powerful.

Our brains process images up to 60,000 times faster than text, so a single graphic can land ideas in milliseconds what paragraphs never will.

Back in the wild, no one texted “lion ahead!”. We saw it and bolted. That split-second of visual processing still rules us today.

That’s why diagrams, flowcharts, and annotated sketches turn complex concepts into “aha!” moments.

If you want to see algorithms in practice, take a look here:

Photon-Lines Substack

Visual-Focused Algorithms Cheat Sheet

Here, I’ll provide a visual-focused overview of some key algorithms used in the real world. Last year, I posted a visual-focused overview of some key data structures which you can find here: Visual Data Structures Cheat Sheet. If you haven’t had a chance to go through it - I highly recommend taking a look. Data and data organization plays a vital role …

Read more

a year ago · 63 likes · Nick M

3) ⚖️ Finding the sweet spot

When tackling optimization problems, you need to evaluate what to use: an exact or a heuristic approach.

So think about…

• 💼 The business: will you be able to translate business needs into mathematical equations? Do the requirements change very frequently? Is it a large problem and you need a solution in a few seconds?

• 🧑🏻‍💻 Software development: you’ll need to debug and verify solutions, but also consider the programming language you’ll use.

• 🔢 Optimization: do you care about getting optimal solutions? About getting high-quality solutions in no time?

The choice of which algorithm to choose has many vertices and in most cases it is not usually a direct decision.

You need to understand the problem, the business needs and the skills of the team.

If you want more of this, read the full article:

Feasible

📈 #13 Finding the sweet spot: when to choose exact vs heuristic algorithms in optimization

According to Aristotle, moral virtue is the golden mean between two extremes…

Read more

2 years ago · 2 likes · Borja Menéndez

Symmetry issues in optimization problems (part C)

Next Monday, I’ll share the rest of the article about symmetry issues in optimization problems.

In the first part, I talked about what they are and classical techniques to address them. In the second one, I talked about advanced techniques.

And in Part C, I’ll cover:

🔬 ML-guided techniques to break symmetries
🛠 Practical ways to dealing with it

I needed to split the article again, it was going to be a huge one!

If you’re dealing with symmetry problems and want to understand ML-guided techniques and useful tips, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) ❌ Don’t solve problems…

If you don’t have them.

I recently did read this article, and it resonated a lot.

Especially after realizing I already wrote this on LinkedIn two months ago.

2) 🎓 Our background

I launched yesterday a poll on LinkedIn to see what your background is, so I’ll be happy to see some more answers! 🙂

Is Industrial Engineering going to win?

3) 💘 When problem-solving becomes a passion

Now that we’re talking about our past…

I recently wrote about mine.

Not only that, I also shared breakthroughs and lessons along the way, why I decided to pursue a PhD and, of course, my initial steps into this world.

Here’s the full article:

Feasible

📈 #71 When problem-solving becomes a passion: my journey into optimization

October, 2012…

Read more

a year ago · 4 likes · Borja Menéndez

Symmetry issues in optimization problems (part B)

Next Monday, I’ll share the rest of the article about symmetry issues in optimization problems.

In the first part, I talked about what they are and classical techniques to address them.

And in Part B, I’ll cover:

🔬 Advanced and ML-guided techniques to break symmetries
🛠 Practical ways to dealing with it

I needed to split the article, it was going to be a huge one!

If you’re dealing with symmetry problems and want to understand advanced techniques and useful tips, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🙅 There’s one thing you don’t need

Unlike in your thesis research, real-world optimization projects rarely require a formal proof of optimality:

• It’s expensive in terms of developing and running times

• Businesses don’t care if you got the optimality or got behind by 1%

• There are a lot of stochastic processes that make it impossible to guarantee optimality

So next time, ask yourself if a good enough solution will do the trick.

2) 🎯 A new challenge

Competitions can be a good place to challenge yourself to learn new concepts or force yourself to give a software solution in no time.

And I just found a new optimization competition in Colombia during this month.

Those are the things we need more of!

3) 🏆 The power of competitions

I’ve seen many competitions in Machine Learning in specific places like Kaggle or AIcrowd.

One of the best things they do is to gather lots of people around a topic, nurturing a community of engaged developers.

They do it through:

• Democratizing problem-solving

• Fostering rapid knowledge exchange

• Bridging the gap between theoretical research and actionable solutions

However, I haven’t seen the same happening in Operations Research.

A centralized platform for competitions in OR would help a lot, really, as today you need to be in touch with all the organizations that creates a challenge (like ASOCIO in Colombia as I said in the previous section).

That’s why I talked about the power of competitions in OR:

Feasible

📈 #56 The power of competitions in Operations Research

Some months ago I had an interesting conversation about competitions in OR…

Read more

a year ago · Borja Menéndez

Symmetry problems in optimization…

Are draining your brain.

When you can assign the same resource to several tasks, and it happens to several resources at the same time, you most probably are facing a symmetry.

They’re a pain as they make your problem more complex than usual. I lived it in my skin.

Next Monday, I’ll share the third part of the 6-part series titled Where did the time go?, where I address several topics around tractability issues in optimization problems.

I’ll cover:

🧱 What symmetry means in optimization
🚨 How it can ruin solver performance
🛠 Practical ways to dealing with it

If you’re facing symmetry problems, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🧲 Attracting people to OR

There’s a rare opportunity to bring more people into Operations Research.

Not through its name.

Not through complex algorithms.

Not through mathematical equations.

But by showing an alternative to the crowded world of Data Science and the increasing pressure on businesses to do more with less, where OR becomes a competitive advantage.

2) ☀️ LSO Summer School 2025

If you’re in India from 16th to 21st of next June, you might consider attending to this summer school on large-scale optimization:

(click on the picture to go to the page)

3) 💡 Reimagining OR

Operations Research has historical systemic issues holding back its growth.

No matter where you start, it leads back to the same place… An inevitable negative feedback loop:

Limited adoption → few case studies → poor marketing → less awareness → low demand → small market → high costs → limited tools → limited adoption…

And it has consequences we’re still facing today.

Why is that? What’s the historical context? And what are the core challenges we must overcome?

Here’s the full breakdown 👇

Feasible

📈 #50 Operations Research reimagined (Part I)

Operations Research stands as…

Read more

a year ago · 4 likes · Borja Menéndez

Are you addressing large-scale optimization problems?

Next Monday, I’ll share the second post on the 6-post series titled “Where did the time go?”.

This time, the focus will be on scalability and I’ll give ideas on how to reduce the complexity of optimization problems with different techniques.

I’ll cover:

🌪️ What large-scale problems are
💡 Ideas to help solving them
👣 First steps to take

If you tackle this kind of problems, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 🔒 Subscribe to the full version: if you aren’t already, consider becoming a paid subscriber. You’ll get access to the full archive, a private chat group, and 30% off new products.

Upgrade subscription

2) 🤝🏻 Collaborate with Feasible. I’m always looking for great products and services that I can recommend to subscribers. Also, if you want to write an article with me, I’m open to that! If you are interested in reaching an audience of Operations Research Engineers, you may want to do that here.

Read more here

3) 📤 Share the newsletter with a friend, and earn rewards in compensation. You’re just one referral away from getting The Modern OR Engineer Playbook: Mindset, methods, and metrics to deliver Optimization that matters.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🍫 Raw enjoyments

An algorithm.

A mathematical model.

A forecast to feed your optimization problem.

Everything you create… If it doesn’t serve the business, it’s just entertainment.

And it’s good. I mean, if you want to have some joy with your code, experiment with algorithms, try new tools, do it! But don’t lie to yourself: a solution that is not in production might feel satisfying, but the business impact is still zero until it’s live.

Pick some orphan code living in your repo or hard drive, and ask yourself: what would have to be true for this to ship next quarter?

If nothing comes to mind, then throw it away, as if it never existed before.

That will give you clarity. Clarity will give you speed.

2) 🔢 State of Mathematical Optimization 2025

I just received an email from Gurobi and thought it’d be a great opportunity for our community to weigh in.

Everyone’s perspective matters. It took me 8 minutes to fill the survey.

3) 🏈 Scheduling the NFL

We tend to think that creating a schedule for a sport league like the NFL is an easy task.

It’s not.

You need to take into account flights, dates, international competitions, specific conditions…

Each new dimension adds complexity to an already complex problem.

It becomes so complex that they need 4000 machines and 3 months of time:

Feasible

📈 #11 OR in Action: What Do 4000 AWS Instances, 3 Months of Time, and the NFL Have in Common?

The scheduling of matches in a sports league is a complex task that requires taking into account a multitude of factors…

Read more

2 years ago · 5 comments · Borja Menéndez

Are you addressing large-scale optimization problems?

Next Monday, I’ll share some ideas on how to reduce the complexity of optimization problems with different techniques.

I’ll cover:

🌪️ What large-scale problems are
💡 Ideas to help solving them
👣 First steps to take

If you tackle this kind of problems, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 📣 Advertise in Feasible. I’m always looking for great products and services that I can recommend to subscribers. If you are interested in reaching an audience of Operations Research Engineers, you may want to advertise here. Just 📨 answer this email 📨 and I’ll get back to you.

2) 📤 Share the newsletter with a friend, and earn rewards in compensation.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🧩 Solver selection

Selecting a solver is a central part of the management process of an OR project.

Identifying optimization problems at your company → translate those business problems into mathematical models → design an optimization solution and select the solver that fits the best → deploy the solution → monitoring the impact on business.

That’s why, if you want to start from one point, you need to know the solvers that exist in the market.

Here I give you a LinkedIn post in which many people collaborated to add their ideas.

And if you want a compass 🧭 to guide you in the process of selecting a solver, here I have a few tips.

2) 🚀 Operationalize OR-Tools

One popular open-source solver within that previous list is Google OR-Tools.

It ships with a purpose-built routing module, and that’s quite good since no heavy math modelling is required.

And it’s also battle-tested, earning gold medals in recent Max-SAT competitions.

Want to see it in action? Nextmv is hosting a live session on Wednesday, 21 May, covering:

• building a data-fetch-run-visualize workflow with OR-Tools

• tips to slot it into your existing stack faster

If you want to know more, click on the picture below!

(this is not a paid collaboration, just genuine interest)

3) 🌉 Bridging the gap

I just read the latest newsletter from HiGHS.

(thanks for the link!)

It discusses GPU accelerated optimization with PDLP, sharing these insights:

The conclusion is that PDLP can solve some LP problems significantly faster than interior point or simplex, and a few HiGHS users have already reported this, but it’s the exception rather than the rule, and the accuracy of the claimed solution must be assessed.

Frankly, I expected the gains to be broader.

Back in October 2024 I shared my early thoughts on this new solver from Google.

If you want to know the theoretical advances it proposes, read the full piece here:

Feasible

📈 #20 PDLP: bridging the gap between traditional solvers and the demands of large-scale optimization

I like writing the issue number at the beginning of the title…

Read more

a year ago · 2 likes · Borja Menéndez

Beyond Gurobi: 3+ years with Google OR-Tools

Looking at the State of Mathematical Optimization Report from Gurobi, most of the problems fall under planning, operational applications, or logistics.

Many of those problems have lots of binary variables, so you can use a different solver like Google OR-Tools for solving them.

Next Monday, I’ll share my experience using it at planning problems.

I’ll cover:

🏆 Why Google OR-Tools (and not others)
💡 Modeling ideas after more than 3 years using it
📚 Workflows and where to look for good documentation

If you’re about to solve a planning problem and you’re looking for a solver, this will be useful. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 📣 Advertise in Feasible. I’m always looking for great products and services that I can recommend to subscribers. If you are interested in reaching an audience of Operations Research Engineers, you may want to advertise here. Just 📨 answer this email 📨 and I’ll get back to you.

2) 📤 Share the newsletter with a friend, and earn rewards in compensation.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🦾 OR is like going to the gym

It's incredibly hard to build muscle, requiring consistent effort, discipline, and time. But stop exercising for just a few weeks, and you'll quickly lose your progress.

Similarly, building an efficient, optimized system in OR takes careful planning, consistency, and sustained effort. Yet neglecting regular maintenance -checking business KPIs, keep users engaged, solve bugs as they happen- can rapidly deteriorate system performance, forcing you into costly emergency interventions.

This is why, just like muscles, optimization demands consistency, not just one-time heroic efforts.

2) 🔊 The Decision Intelligence Lab

Just a couple of days ago, Prof. Vijay Mehrotra shared he’s launching a podcast together with Prof. .

And I’m excited about it as this is the type of content about OR we need.

Each episode features leaders sharing how smarter decisions are reshaping business and technology.

They already featured Irv Lustig, Doug Gray, and Laura Albert!

3) ⚖️ How to choose exact vs heuristics

When solving an optimization problem, you need to choose how you’re going to solve it.

One way is assessing the necessity of proofs for optimality.

But in any case there are more actors involved, like the business and even the software development process.

How do you balance everything in the equation so that you give the most value to the business as quickly as possible?

You can read the full article here:

Feasible

📈 #13 Finding the sweet spot: when to choose exact vs heuristic algorithms in optimization

According to Aristotle, moral virtue is the golden mean between two extremes…

Read more

2 years ago · 1 like · Borja Menéndez

🚦 Why breaking tests make me happy

Next Monday, I’ll share the why behind Test-Driven Development in Operations Research.

Or what I call Test-Driven Modeling.

I’ll cover:

🔍 Why tests are so important in OR
🧰 The types of tests you can run
🚀 How to leverage TDD on your next project

If you build, maintain, or just love optimization models, this one’s for you. See you Monday! 🙌

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 📣 Advertise in Feasible. I’m always looking for great products and services that I can recommend to subscribers. If you are interested in reaching an audience of Operations Research Engineers, you may want to advertise here. Just 📨 answer this email 📨 and I’ll get back to you.

2) 📤 Share the newsletter with a friend, and earn rewards in compensation.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 🕶️ Optimization is everywhere, like The Matrix

I see optimization everywhere.

Literally.

If we could take that famous quote from The Matrix and just replace those words with optimization, you'd pretty much know what's going on inside my head:

Optimization is everywhere.

It is all around us.

Even now, in this very room.

You can see it when you look out your window or when you turn on your television.

You can feel it when you go to work... when you go to church... when you pay your taxes.

It is the world that has been pulled over your eyes to blind you from the truth.

Despite I’m a big fan of The Matrix, probably (and provably) the best science fiction movie of all time, I must say those previous lines do have a point, don’t they?

I mean, I just realized I can see SAT problems…

In my bathroom 😅

(picture taken just a few days ago)

2) ✨ Using SAT solving for Quantum Computing

Efficient quantum circuit mapping gives you:

• Fewer errors

• Shorter runtimes

• Better use of quantum resources

Why? Because…

1. 🧠 Quantum computers have physical constraints

Quantum processors (like those from IBM, Rigetti, or Amazon Braket) have qubits arranged in a specific topology.

But not all qubits can interact directly.

So, if your algorithm requires interactions between qubits that aren’t directly connected, you need to insert extra swap gates to bring them together.

(These swaps are costly (in time and in error rate), so minimizing them is crucial.)

2. ⚙️ Every extra gate increases error

Quantum gates are not perfect, they introduce noise.

More gates = higher chance the entire computation will fail before it finishes.

Especially on NISQ (Noisy Intermediate-Scale Quantum) devices, the fewer the gates, the better.

NISQ devices are the current generation of quantum computers.

3. ⏱️ Execution time matters

Quantum states decohere over time.

(Decoherence is what happens when a quantum state starts to lose its "quantumness" over time.)

Shorter circuits are more likely to complete before decoherence ruins the computation.

Faster compilation → quicker experimentation and iteration.

4. 📈 Scalability

As quantum algorithms get more complex, mapping them efficiently is the only way to scale.

Better mapping = more powerful algorithms on limited hardware.

🎯 So…

Enhancing the efficiency of mapping quantum computations onto physical quantum circuits is crucial.

And Amazon is using SAT solving to optimize quantum circuit mapping.

3) 🔍 How to identify optimization problems

Identifying optimization problems early can be the difference between smooth scaling… Or costly chaos later.

So where should you look?

1. Identify bottlenecks, inefficiencies, or excessive costs (like long leading times in logistics).

2. Look for complex decision-making environments (like assigning a big staff to shifts under several constraints).

3. Consider scalability and impact (if throwing more people at the problem isn’t helping, that’s a sign).

In order to accomplish that, you’ll need to…

1. Start with processes, asking questions about decisions.

2. Collect data and define problems.

3. Get external perspectives, like consultants, peers, or even your customers.

Want the full breakdown? I covered it in a past post 👇

Feasible

📈 #37 How to identify optimization problems at your company

I know it, you know it too, but there’s a lot of people out there that don’t know it yet…

Read more

2 years ago · 2 likes · Borja Menéndez

What if one of the top three conferences in AI and ML…

Had a lot to say in Operations Research?

📅 Next Monday, I’ll share some of the most interesting papers being presented this year at ICLR, the International Conference on Learning Representations.

It’s one of the most influential AI conferences in the world; a clear window into what’s coming next in the field.

And this year, several papers are diving into OR-related topics.

👀 If you want to stay ahead of the curve, this roundup will be worth your time. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 📣 Partner with Feasible. I’m always looking for great products and services that I can recommend to subscribers. If you are interested in reaching an audience of Operations Research Engineers, you may want to advertise here. Just 📨 answer this email 📨 and I’ll get back to you.

2) 📤 Share the newsletter with a friend, and earn rewards in compensation.

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Have a nice day ahead ☀️

Borja.

Let’s dive in! 🪂

1) 📊 A poll: what path…?

I’m curious to know: what path did you take (or plan to take) after studying Operations Research?

There might be several ones, and though I have my own bet, I’d like to better understand the whole picture.

If you want, you can just answer the poll here:

(click on the image below to go to the post)

2) 🎨 Graph coloring open-source library in Python

Do you work on Graph coloring problems?

Existing open-source options for graph colouring are limited.

Current resources tend to either use simple constructive heuristics that lead to low-quality solutions, or exponential-time exact algorithms that cannot cope with larger graphs.

There are also few open-source options for edge colouring, equitable colouring, and solution visualisation, and, to our knowledge, no options for face colouring, weighted colouring, or pre-colouring.

The GCol library features routines for all of the above.

Check it out!

(click on the image below to go to the post)

3) 🌐 What does Graph Theory reveal about social media?

Cristiano Ronaldo, Justin Bieber, Barack Obama, Elon Musk… All of them over 100 million X (formerly Twitter) followers.

Despite their massive followings, none of these mega-accounts appears in the top 20 most-liked tweets. Meanwhile, Macaulay Culkin, with less than a million followers, garnered nearly 3 million likes on a single tweet in 2020.

This fact reveals something interesting: influence has more to do with network position than sheer popularity.

If you’ve ever wondered why some messages go viral and others don’t -or how graphs quietly shape the world around us- this is for you.

Let’s dive back in 👇

Feasible

📈 #28 Who is an influencer... According to Graph Theory?

What do the Twitter accounts of Cristiano Ronaldo, Justin Bieber, Barack Obama, and Elon Musk have in common…

Read more

2 years ago · Borja Menéndez

Does this journey reflect your own one?

Next Monday, I’ll share my very own journey into optimization.

A path where metaheuristics were my starting point and constant companion.

I’ll cover:

👣 My initial steps into metaheuristics
🎓 Why I decided to pursue a PhD in the field
📚 Key breakthroughs and lessons learned along the way

Whether you're just starting out or already on your optimization journey, I hope my experience offers some valuable insights. See you Monday!

And that’s it for today!

If you’re finding this newsletter valuable, consider doing any of these:

1) 📣 Advertise in Feasible. I’m always looking for great products and services that I can recommend to subscribers. If you are interested in reaching an audience of Operations Research Engineers, you may want to advertise here. Just 📨 answer this email 📨 and I’ll get back to you.

2) 📤 Share the newsletter with a friend, and earn rewards in compensation.

Refer a friend

If you have any comments or feedback, just respond to this email!

Have a nice day ahead ☀️

Borja.