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#optimization
4 posts4 participants0 posts today
Made some #optimization changes to my #UPBGE game #DanisRace
Optimizing Node.js Performance: V8 Memory Management and GC Tuning, by @mcollina (@platformatic):
https://blog.platformatic.dev/optimizing-nodejs-performance-v8-memory-management-and-gc-tuning
'On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations', by Antoine Godichon-Baggioni, Nicklas Werge.
http://jmlr.org/papers/v26/23-1565.html
#streaming #stochastic #optimization
The largest road network TSP instance with 81,998 locations solved optimally was crafted using OSRM! A groundbreaking milestone in computational mathematics. Check out the details here: https://www.math.uwaterloo.ca/tsp/korea/index.html #TSP #Optimization #OSRM
www.math.uwaterloo.caKorea TSPs
Shortest walking tour to 81,998 bars in Korea – TSP solved in 178 days
www.math.uwaterloo.caKorea TSPs
5 Ways to Reduce HTML File Size on Your Website, by @debugbear.com:
www.debugbear.com · 5 Ways to Reduce HTML File Size on Your Website | DebugBearHere is how you can reduce HTML file size to improve web performance and Core Web Vitals on your website.
ClickHouse gets lazier (and faster): Introducing lazy materialization
https://clickhouse.com/blog/clickhouse-gets-lazier-and-faster-introducing-lazy-materialization
ClickHouseClickHouse gets lazier (and faster): Introducing lazy materializationClickHouse learned to procrastinate strategically. Discover how lazy materialization skips unnecessary column reads to accelerate queries.
'Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization', by Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng.
http://jmlr.org/papers/v26/22-0523.html
#regret #optimization #minimization
5 Ways to Reduce HTML File Size on Your Website, Step 0, by @j9t [@frontenddogma.com]:
meiert.com5 Ways to Reduce HTML File Size on Your Website, Step 0 · Jens Oliver Meiert
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Jens Oliver Meiert
Efficient E-Matching for Super Optimizers
https://blog.vortan.dev/ematching/
#HackerNews #Efficient #E-Matching #Super #Optimizers #E-Matching #Technology #Optimization
blog.vortan.devSinon's Blog
'Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning', by Kuangyu Ding, Jingyang Li, Kim-Chuan Toh.
http://jmlr.org/papers/v26/23-0657.html
#gradient #gradients #optimization
Cutting down Rust compile times from 30 to 2 minutes with one thousand crates
www.feldera.comCutting Down Rust Compile Times From 30 to 2 Minutes With One Thousand CratesWe'll explore how we can leverage all cores on a machine with rustc using many crates to speed up compilation.
How to Optimize Rust for Slowness: Inspired by New Turing Machine Results
https://medium.com/@carlmkadie/how-to-optimize-your-rust-program-for-slowness-eb2c1a64d184
Medium · How to Optimize your Rust Program for Slowness - Carl M. Kadie - Medium
Fibonacci Hashing: The Optimization That the World Forgot
Probably Dance · Fibonacci Hashing: The Optimization that the World Forgot (or: a Better Alternative to Integer Modulo)I recently posted a blog post about a new hash table, and whenever I do something like that, I learn at least one new thing from my comments. In my last comment section Rich Geldreich talks about h…
Do you need better performance than what the standard #tidyverse functions have? {collapse} might be worth a look: https://sebkrantz.github.io/collapse/ #rstats #optimization
sebkrantz.github.ioAdvanced and Fast Data Transformation in RA large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as units, integer64, xts/zoo, tibble, grouped_df, data.table, sf, and pseries/pdata.frame.