The hottest Optimization Substack posts right now

And their main takeaways
Category
Top Technology Topics

All paths point downhill

arg min • 218 implied HN points • 31 Oct 24
🕹 Technology Algorithms Optimization Data science Machine Learning Mathematics
  1. In optimization, there are three main approaches: local search, global optimization, and a method that combines both. They all aim to find the best solution to minimize a function.
  2. Gradient descent is a popular method in optimization that works like local search, by following the path of steepest descent to improve the solution. It can also be viewed as a way to solve equations or approximate values.
  3. Newton's method, another optimization technique, is efficient because it converges quickly but requires more computation. Like gradient descent, it can be interpreted in various ways, emphasizing the interconnectedness of optimization strategies.

Basic Linear Algebra Subprogramming

arg min • 178 implied HN points • 29 Oct 24
🕹 Technology Computing Mathematics Optimization Data science Algorithms
  1. Understanding how optimization solvers work can save time and improve efficiency. Knowing a bit about the tools helps you avoid mistakes and make smarter choices.
  2. Nonlinear equations are harder to solve than linear ones, and methods like Newton's help us get approximate solutions. Iteratively solving these systems is key to finding optimal results in optimization problems.
  3. The speed and efficiency of solving linear systems can greatly affect computational performance. Organizing your model in a smart way can lead to significant time savings during optimization.

The Shape of Stats to Come

arg min • 634 implied HN points • 10 Oct 24
🚌 Education Statistics Optimization Philosophy Data Analysis Mathematics
  1. Statistics often involves optimizing methods to get the best results. Many statistical techniques can actually be viewed as optimization problems.
  2. Choosing a statistical method isn't just about the math—it's also based on beliefs about reality. This philosophical side is important but often overlooked.
  3. There's a danger in relying too much on tools and models we can solve. Sometimes, we force the data to fit our preferred methods instead of being open to the actual complexities.

Designed Interactions

arg min • 257 implied HN points • 15 Oct 24
🚌 Education Statistics Optimization Mathematics Machine Learning
  1. Experiment design is about choosing the right measurements to get useful data while reducing errors. It's important in various fields, including medical imaging and randomized trials.
  2. Statistics play a big role in how we analyze and improve measurement processes. They help us understand the noise in our data and guide us in making our experiments more reliable.
  3. Optimization is all about finding the best way to minimize errors in our designs. It's a practical approach rather than just seeking perfection, and we need to accept that some questions might remain unanswered.

Convex Optimization at the Midpoint

arg min • 198 implied HN points • 17 Oct 24
🚌 Education Optimization Programming Statistics Algorithms
  1. Modeling is really important in optimization classes. It's better to teach students how to set up real problems instead of just focusing on abstract theories.
  2. Introducing programming assignments earlier can help students understand optimization better. Using tools like cvxpy can make solving problems easier without needing to know all the underlying algorithms.
  3. Convex optimization is heavily used in statistics, but there's not much focus on control systems. Adding a section on control applications could help connect optimization with current interests in machine learning.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Interpolation Is All You Need

arg min • 317 implied HN points • 08 Oct 24
🕹 Technology AI Optimization Machine Learning Data science
  1. Interpolation is a process where we find a function that fits a specific set of input and output points. It's a useful tool for solving problems in optimization.
  2. We can build more complex function fitting problems by combining simple interpolation constraints. This allows for greater flexibility in how we define functions.
  3. Duality in convex optimization helps solve interpolation problems, enabling efficient computation and application in areas like machine learning and control theory.

Fine-tuning LLMs with 32-bit, 8-bit, and Paged AdamW Optimizers

The Kaitchup – AI on a Budget • 259 implied HN points • 07 Oct 24
🕹 Technology AI Machine Learning Optimization Data processing Programming
  1. Using 8-bit and paged AdamW optimizers can save a lot of memory when training large models. This means you can run more complex models on cheaper, lower-memory GPUs.
  2. The 8-bit optimizer is almost as effective as the 32-bit version, showing similar results in training. You can get great performance with less memory required.
  3. Paged optimizers help manage memory efficiently by moving data only when needed. This way, you can keep training even if you don't have enough GPU memory for everything.

An Inversion Cookbook

arg min • 297 implied HN points • 04 Oct 24
🚌 Education Mathematics Statistics Optimization Regression Data science
  1. Using modularity, we can tackle many inverse problems by turning them into convex optimization problems. This helps us use simple building blocks to solve complex issues.
  2. Linear models can be a good approximation for many situations, and if we rely on them, we can find clear solutions to our inverse problems. However, we should be aware that they don't always represent reality perfectly.
  3. Different regression techniques, like ordinary least squares and LASSO, allow us to handle noise and sparse data effectively. Tuning the right parameters can help us balance accuracy and manageability in our models.

Attracted To The Desert (Or The Forest)

Software Design: Tidy First? • 1634 implied HN points • 12 Nov 24
🕹 Technology Software Design Development Practices Optimization Collaboration
  1. Software development has different styles that often lead to similar outcomes, guided by underlying trends called attractors. These attractors influence how teams change over time, pulling them towards certain approaches.
  2. It’s not just about adding more value in software projects. Instead, the focus should be on removing waste and improving efficiency in how teams work together.
  3. The environment where a team operates, whether it's a productive forest or a limiting desert, greatly affects their potential for growth. The forest offers more opportunities for improvement than the desert.

Inverse frontiers

arg min • 158 implied HN points • 07 Oct 24
🕹 Technology Optimization Machine Learning Algorithms Data science Artificial Intelligence
  1. Convex optimization has benefits, like collecting various modeling tools and always finding a reliable solution. However, not every problem fits neatly into a convex framework.
  2. Some complex problems, like dictionary learning and nonlinear models, often require nonconvex optimization, which can be tricky to handle but might be necessary for accurate results.
  3. Using machine learning methods can help solve inverse problems because they can learn the mapping from measurements to states, making it easier to compute solutions later, though training the model initially can take a lot of time.

Song Pong

Victor Tao • 273 HN points • 28 Aug 24
🕹 Technology Gaming Optimization Music Visualization Programming
  1. You can make a pong game more exciting by syncing the ball's movements to music. This allows paddles to dance to the beat as they hit the ball.
  2. Using math and optimization techniques can help you decide where the paddles should hit the ball. It ensures that the game looks good while still following all the rules.
  3. Changing the physics of the game doesn't have to be hard. You just update the rules in your math model, making it easy to test new ideas and keep improving the game.

Bridging the Gap: From Statistical Distributions to Machine Learning Loss Functions

Mindful Modeler • 818 implied HN points • 14 Nov 23
🕹 Technology Machine Learning Statistical Analysis Optimization
  1. Understanding the distribution of the target variable is key in choosing statistical analysis or machine learning loss functions.
  2. Certain loss functions in machine learning correspond to maximum likelihood estimation for specific distributions, creating a bridge between statistical modeling and machine learning.
  3. While connecting distributions to loss functions is insightful, the real power in machine learning lies in the flexibility to design custom loss functions rather than being constrained by specific distributions.

7 perspectives on machine learning

Mindful Modeler • 279 implied HN points • 09 Apr 24
🕹 Technology Machine Learning Data interpretation Automation Optimization Computing
  1. Machine learning is about building prediction models. It covers a wide range of applications, but may not be perfect for unsupervised learning.
  2. Machine learning is about learning patterns from data. This view is useful for understanding ML projects beyond just prediction.
  3. Machine learning is automated decision-making at scale. It emphasizes the purpose of prediction, which is to facilitate decision-making.

You need to neglect mostly everything to win big

Play Permissionless • 319 implied HN points • 18 Mar 24
💼 Business Entrepreneurship Strategy Productivity Management Optimization
  1. To win big, you only need to get a small number of things right and can afford to mess up everything else. This applies to both companies and individuals.
  2. Winning big often requires unlearning traditional schooling strategies and focusing on doing a great job at a few key aspects while neglecting the rest.
  3. Removing non-essential tasks and focusing solely on what helps deliver better and faster results can lead to significant improvements and ultimately winning big.

Workshop on Performance Optimizations in Code: The One Billion Row Challenge

Confessions of a Code Addict • 577 implied HN points • 15 Jan 24
🕹 Technology Programming Workshop Optimization Performance Coding
  1. Code efficiency at scale is crucial - data structures and algorithms matter, but execution cost is also important.
  2. Participating in challenges like the 1 Billion Row Challenge can enhance performance engineering skills.
  3. The workshop covers optimization techniques like flamegraphs, I/O strategies, system calls, SIMD instructions, and more.

Age of Invention: Cash Cows

Age of Invention, by Anton Howes • 1008 implied HN points • 10 Aug 23
💾 History Industrial Revolution Innovation Agriculture Optimization
  1. Robert Bakewell had an 'improving mentality' when it came to breeding animals, focusing on optimizing profit and efficiency.
  2. Bakewell selectively bred cows and sheep to maximize valuable meat and minimize feeding costs.
  3. The improving mentality led Bakewell to continuously optimize all aspects of his farm, from animal breeding to farm layout and operations.

A Guide to Optimising your Spark Application Performance (Part 1).

SwirlAI Newsletter • 432 implied HN points • 02 Jul 23
🕹 Technology Data processing Optimization Performance Distributed Computing
  1. Understanding Spark architecture is crucial for optimizing performance and identifying bottlenecks.
  2. Differentiate between narrow and wide transformations in Spark, and be cautious of expensive shuffle operations.
  3. Utilize strategies like partitioning, bucketing, and caching to maximize parallelism and performance in Spark applications.

A quick introduction to Memory Pools [Math Mondays]

Technology Made Simple • 179 implied HN points • 27 Feb 24
🕹 Technology Memory management Software Engineering Optimization Performance Programming
  1. Memory pools are a way to pre-allocate and reuse memory blocks in software, which can significantly enhance performance.
  2. Benefits of memory pools include reduced fragmentation, quick memory management, and improved performance in programs with frequent memory allocations.
  3. Drawbacks of memory pools include fixed-size blocks, overhead in management, and potential for memory exhaustion if not carefully managed.

A Guide to Optimising your Spark Application Performance (Part 2)

SwirlAI Newsletter • 314 implied HN points • 06 Aug 23
🕹 Technology Programming Big Data Optimization Data Storage
  1. Choose the right file format for your data storage in Spark like Parquet or ORC for OLAP use cases.
  2. Understand and utilize encoding techniques like Run Length Encoding and Dictionary Encoding in Parquet for efficient data storage.
  3. Optimize Spark Executor Memory allocation and maximize the number of executors for improved application performance.

Writing Good Multi-threaded Programs: Ensuring Correctness and Optimality

Arpit’s Newsletter • 157 implied HN points • 05 Apr 23
🕹 Technology Programming Concurrency Optimization Best Practices
  1. Ensuring correctness in multi-threaded programs is crucial; use locking and atomic instructions to prevent race conditions.
  2. For optimality, ensure fairness among threads and efficient logic to avoid bottlenecks.
  3. Divide workload evenly among threads or use a global variable to track progress for efficient results.

Find Optimal Learning Rates for Stable Diffusion Fine-tunes

followfox.ai’s Newsletter • 157 implied HN points • 13 Mar 23
🕹 Technology Machine Learning Data Analysis Experimentation Optimization Model Training
  1. Estimate the minimum and maximum learning rate values by observing when the loss decreases and increases during training.
  2. Choosing learning rates within the estimated range can optimize model training.
  3. Validating learning rate ranges and fine-tuning with different datasets can improve model flexibility and accuracy.

A fun thought on optimization

Sunday Letters • 79 implied HN points • 22 Jan 24
🕹 Technology AI Design Optimization Product Development
  1. Avoid optimizing too early in the design process. This can lead to wasted efforts and complicated designs.
  2. In the world of AI, focusing too much on costs can lead to weak solutions. It's better to have a solid, simple design from the start.
  3. Instead of worrying about future needs, consider how hard it will be to make changes later. It's important to find a balance between planning and flexibility.

Understanding Branchless Programming [Technique Tuesdays]

Technology Made Simple • 119 implied HN points • 26 Jul 23
🕹 Technology Programming Performance Debate Complexity Optimization
  1. Branchless programming is a technique that minimizes the use of branches in code to avoid performance penalties.
  2. Branchless programming can offer optimization benefits, but its complexity can outweigh the performance gains and make code maintenance challenging.
  3. Simpler code is often better than overly complex code, and branchless programming may not be suitable for most developers despite its potential performance improvements.

3 Techniques to help you optimize your code bases[Technique Tuesdays]

Technology Made Simple • 119 implied HN points • 26 Apr 23
🕹 Technology Coding Optimization Software Engineering Developer Tools
  1. Compile time evaluation can help execute functions at compile time instead of run time, saving memory and CPU time.
  2. Dead code elimination removes unused code, enhancing code readability and reducing executable size.
  3. Strength reduction is a compiler optimization technique that replaces expensive operations with simpler ones, making localized code changes easier.

From AARRR to BBB funnels

Gentle Nudge • 19 implied HN points • 28 May 24
💼 Business Marketing Optimization Behavior Benefits
  1. Funnel optimization involves analyzing stages, generating hypotheses, and considering user feedback to improve user experience.
  2. The 3B framework, focusing on Behavior, Barriers, and Benefits, helps adjust products from the users' perspective for better engagement.
  3. Identify potential barriers in the user journey, offer small incentives, like progress indicators, and align call-to-actions with expected results to enhance user motivation.

D-Adaptation: Goodbye Learning Rate Headaches?

followfox.ai’s Newsletter • 98 implied HN points • 21 Jun 23
🕹 Technology Artificial Intelligence Machine Learning Optimization Comparative Analysis
  1. D-Adaptation method automates setting learning rate, aiming for optimal convergence in machine learning.
  2. Implementing D-Adaptation can consume more VRAM and result in slower training speed compared to other optimizers.
  3. Initial results show D-Adaptation performing comparably to hand-picked parameters in generating high-quality models.

Exphormer(Graph Neural Networks)

MLOps Newsletter • 39 implied HN points • 04 Feb 24
🕹 Technology Machine Learning Neural Networks Optimization Deep Learning Library
  1. Graph transformers are powerful for machine learning on graph-structured data but face challenges with memory limitations and complexity.
  2. Exphormer overcomes memory bottlenecks using expander graphs, intermediate nodes, and hybrid attention mechanisms.
  3. Optimizing mixed-input matrix multiplication for large language models involves efficient hardware mapping and innovative techniques like FastNumericArrayConvertor and FragmentShuffler.