The hottest Optimization Substack posts right now

And their main takeaways
Category
Top Technology Topics

AGI is a wrong goal

Yuxi’s Substack • 0 implied HN points • 17 Jul 23
🕹 Technology Optimization
  1. Multi-objective optimization can't optimize all objectives.
  2. More and tighter constraints make finding a solution harder.
  3. Tasks should be divided into groups and handled collaboratively, rather than expecting a single model to do everything.

Polymath Engineer Weekly #82

Polymath Engineer Weekly • 0 implied HN points • 19 Feb 24
🕹 Technology Optimization
  1. Feedback surveys can help in improving a site by collecting user opinions
  2. Systems like Ledger at Stripe track money movement effectively using vtables and monitoring
  3. Understanding different knowledge levels in health can categorize people into groups for better health practices

[Solution]Problem 17: Minimum cost of painting N houses up to K different colors.[Facebook]

Technology Made Simple • 0 implied HN points • 23 Dec 21
🕹 Technology Optimization
  1. The problem involves minimizing cost while ensuring no neighboring houses have the same color. This can be represented using a matrix.
  2. Brute force can be initially used to explore all combinations, but dynamic programming is a more efficient approach in this scenario. Dynamic programming optimizes calculations by avoiding unnecessary computations.
  3. By utilizing dynamic programming, we can efficiently calculate the minimum cost of painting the houses with different colors. This method involves maintaining a matrix cache to track the costs and ensure the color constraint is met.

Why you should implement Evolutionary Algorithms in your Machine Learning Projects

Technology Made Simple • 0 implied HN points • 22 Dec 21
🕹 Technology Optimization
  1. Evolutionary Algorithms are underutilized in Machine Learning Research and can be powerful tools to solve complex problems.
  2. Evolutionary Algorithms provide flexibility by not requiring differentiable functions, making them suitable for a variety of real-world optimization problems.
  3. Evolutionary Algorithms can outperform more expensive gradient-based methods, as demonstrated in various research projects including Google's AutoML-Zero.

PostgreSQL Sort estimation instability

Conserving CPU's cycles ... • 0 implied HN points • 26 Jun 24
🕹 Technology Optimization
  1. Incremental sort was added in PostgreSQL 2020 to enhance sorting strategies and improve efficiency in handling large datasets and analytical queries.
  2. Estimation instability in PostgreSQL's sort operations can lead to unexpected query plans and performance differences, emphasizing the importance of careful estimation.
  3. The vulnerability in PostgreSQL's optimizer code showcases how the choice of expression evaluation can impact query performance, highlighting a need for optimization improvements.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

MSSQL query plan optimisation advantages

Conserving CPU's cycles ... • 0 implied HN points • 21 May 24
🕹 Technology Optimization
  1. In MSSQL to PostgreSQL migrations, challenges like query slowdowns may arise, with some queries taking significantly longer to execute in PostgreSQL compared to MSSQL.
  2. Join algorithm selection and parallelism are two key advantages contributing to MSSQL's impressive query execution speed.
  3. Multi-clause selectivity estimation in MSSQL allows for more precise cardinality estimation in complex join queries, giving it an edge over PostgreSQL in certain scenarios.

PostgreSQL Asymmetric Join technique as a Further Evolution of Partitionwise Join

Conserving CPU's cycles ... • 0 implied HN points • 05 May 24
🕹 Technology Optimization
  1. The Asymmetric Join (AJ) technique in PostgreSQL allows for more efficient parallel append operations by individually connecting each partition with a non-partitioned relation and merging results.
  2. One advantage of the Asymmetric Join technique is the independent choice of join strategy for each partition, leading to improved table scan filtering and reduced hash table sizes.
  3. Considerations for implementing the Asymmetric Join include growing search space for plans, restrictions on the inner and outer relations, and the necessity of checking partitioning schemes for different plain and partitioned relation combinations.

An Introduction to Genetic Algorithms

Tecnica • 0 implied HN points • 28 Jul 24
🕹 Technology Optimization
  1. Genetic algorithms mimic natural evolution. They start with random solutions and improve them through processes like crossover and mutation to find better answers to problems.
  2. A genetic algorithm works by creating a group of solutions and then mixing and matching them to form new solutions. The best-performing solutions are kept for the next generation.
  3. While genetic algorithms are easy to implement and can explore many options at once, they might not always find the best solution quickly and can be tricky to set up because of the need for a good fitness function.

Explaining ResNets in simple, plain language

Jon’s Substack • 0 implied HN points • 25 Mar 24
🕹 Technology Optimization
  1. ResNets help make deep neural networks easier to train by smoothing the loss landscape. This makes it simpler for optimization algorithms to find the best solutions.
  2. The main idea behind ResNets is to add 'skip connections' between layers, allowing the network to learn identity functions. This means that if a layer isn’t helpful, it won't negatively impact learning.
  3. As networks get deeper, ResNets adjust their weights to limit changes in representations. This keeps the performance consistent, preventing problems like overfitting and improving accuracy.

Six Billion Unnecessary Function Calls

Andrew’s Substack • 0 implied HN points • 10 Oct 24
🕹 Technology Optimization
  1. Focus on adding features before trying to optimize your code, unless performance is a big issue. It's better to develop first and deal with optimization later.
  2. Low-level optimizations are useful for compilers, but many developers may not gain much from them. It's often smarter to enable existing optimizations like `O3` for better performance.
  3. High-level optimizations, like rethinking your code structure, help everyone. They improve performance and make the code easier to understand.

Using Genetic Algorithms to solve any problem

The Halfway Point • 0 implied HN points • 26 Apr 24
🕹 Technology Optimization
  1. Genetic algorithms are useful tools for solving various problems because they adapt well and can be implemented easily. They help find good solutions, even if those solutions aren't always the absolute best.
  2. When using genetic algorithms, it's important to define three key elements: the system, the cost function, and how the system should change to minimize costs. This helps organize and optimize the problem-solving process.
  3. The DEAP library for Python makes it simple to create and manage genetic algorithms. It provides tools to easily track progress and make the necessary adjustments during the optimization process.

Algorithms for Optimization (Explained Simply): Part 2 - Line Search and the Trust Region Method

Photon-Lines Substack • 0 implied HN points • 25 Oct 24
🕹 Technology Optimization
  1. The Line Search method helps find a minimum by choosing a direction to step and adjusting step size until a local minimum is reached. It's like walking downhill one small step at a time.
  2. Approximate line search is quicker and doesn’t require finding the perfect step size. Instead, it focuses on taking good enough steps to keep moving closer to the minimum without wasting time.
  3. The Trust Region method keeps steps within a 'trust zone' where the function behaves predictably. If the prediction is accurate, the zone expands; if not, it shrinks, helping to avoid large, risky moves.

SaaS Funnels - What's holding you back?

Organic SaaS Growth • 0 implied HN points • 29 Oct 24
💼 Business Optimization
  1. Many SaaS funnels are underperforming, which is a common issue that needs attention.
  2. There's a need for guides that focus on practical and actionable steps to improve SaaS funnels.
  3. Understanding specific challenges and questions around funnel optimization can help create better resources.

Smarter Reasoning, Efficient Fine-Tuning, and Learning with LLMs

HackerPulse Dispatch • 0 implied HN points • 27 Dec 24
🕹 Technology Optimization
  1. OREO uses offline reinforcement learning to help language models improve multi-step reasoning for tasks like math and control, making them smarter and less data-hungry.
  2. Memory layers make models more efficient by using key-value lookups, which can cut computational costs in half while maintaining performance even at a large scale.
  3. LoHan allows fine-tuning of huge models on regular GPUs, making the process cheaper and more effective, while LearnLM enhances teaching capabilities of AI, making it a preferred choice among educational tools.