Tootfinder

It's not too late to submit talk/performance/workshop proposals to @… 2025! https://2025.algorithmicpattern.org/call/

Synthesizer Arturia MiniFreak: Firmware-Update macht ihn zum Vocoder
Arturia spendiert dem MiniFreak das Update auf Version 4.0.0. Damit wird der "algorithmische Synthesizer" zum Vocoder.

For that matter, the #llms are also invited to feast on my Allegretto For Orchestra 😊
#AI for over two decades!

I really like this approach
https://infosec.exchange/@masek/114624183034377452

This https://arxiv.org/abs/2506.02635 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Efficient Quadratic Corrections for Frank-Wolfe Algorithms
We develop a Frank-Wolfe algorithm with corrective steps, generalizing previous algorithms including blended pairwise conditional gradients and fully-corrective Frank-Wolfe, and propose a highly efficient corrective algorithm in the case of convex quadratic objectives based on linear optimization or linear system solving, akin to Wolfe's minimum-norm point. Beyond optimization problems that are directly quadratic, we revisit two algorithms, split conditional gradient for the intersection of two…

This https://arxiv.org/abs/2505.17836 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means
This paper addresses the problem of robust estimation in gossip algorithms over arbitrary communication graphs. Gossip algorithms are fully decentralized, relying only on local neighbor-to-neighbor communication, making them well-suited for situations where communication is constrained. A fundamental challenge in existing mean-based gossip algorithms is their vulnerability to malicious or corrupted nodes. In this paper, we show that an outlier-robust mean can be computed by globally estimating …

This https://arxiv.org/abs/2506.02491 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

On the Inversion Modulo a Power of an Integer
Recently, Koc proposed a neat and efficient algorithm for computing $x = a^{-1} \pmod {p^k}$ for a prime $p$ based on the exact solution of linear equations using $p$-adic expansions. The algorithm requires only addition and right shift per step. In this paper, we design an algorithm that computes $x = a^{-1} \pmod {n^k}$ for any integer $n>1$. The algorithm has a motivation from the schoolbook multiplication and achieves both efficiency and generality. The greater flexibility of our algorithm …

This https://arxiv.org/abs/2505.23585 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

On-Policy RL with Optimal Reward Baseline
Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement learning algorithms often suffer from training instability due to loose on-policy constraints and computational inefficiency due to auxiliary models. In this work, we propose On-Policy RL with Optimal reward baseline (OPO), a novel and simplified reinforcement learning algorithm designed to address these challenges. O…

A post from the archive 📫:
Algorithms of oppression
#musings

This https://arxiv.org/abs/2503.13965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

First-Order Projected Algorithms With the Same Linear Convergence Rate Bounds as Their Unconstrained Counterparts
In this paper, we propose a systematic approach for constructing first-order projected algorithms for optimization problems with general set constraints, building upon their unconstrained counterparts. We show that these projected algorithms retain the same linear convergence rate bounds, when the latter are obtained for the unconstrained algorithms via quadratic Lyapunov functions arising from integral quadratic constraint (IQC) characterizations. The projected algorithms are constructed by ap…