Tootfinder

Classical algorithm inspired by the feedback-based algorithm for quantum optimization and local counterdiabatic driving
Takuya Hatomura
https://arxiv.org/abs/2506.09214

Classical algorithm inspired by the feedback-based algorithm for quantum optimization and local counterdiabatic driving
We propose a quantum-inspired classical algorithm for combinatorial optimization problems, named the counterdiabaticity-assisted classical algorithm for optimization (CACAO). In this algorithm, a solution of a given combinatorial optimization problem is heuristically searched with classical spin dynamics based on quantum Lyapunov control of local counterdiabatic driving. We compare the performance of CACAO with that of quantum time-evolution algorithms, i.e., quantum annealing, the feedback-bas…

One place I don't mind algorithms is in music apps. I just got Weird Al, Korn, Tiesto, Lynyrd Skynyrd, and Dr. Dre. That's one well tuned algorithm.

A look at Amsterdam's failed experiment to create a fair welfare AI system that would evaluate every welfare applicant in the city for potential fraud (MIT Technology Review)
https://www.technologyreview.com/2025/06/1

Inside Amsterdam’s high-stakes experiment to create fair welfare AI
The Dutch city thought it could break a decade-long trend of implementing discriminatory algorithms. Its failure raises the question: can these programs ever be fair?

I'm looking for a few additional reviewers for Algorithmic Pattern talks/papers . Please let me know if you're up for that!
https://2025.algorithmicpattern.org/call/

An $O(n$)-Algorithm for the Higher-Order Kinematics and Inverse Dynamics of Serial Manipulators using Spatial Representation of Twists
Andreas Mueller
https://arxiv.org/abs/2506.10686

An $O(n$)-Algorithm for the Higher-Order Kinematics and Inverse Dynamics of Serial Manipulators using Spatial Representation of Twists
Optimal control in general, and flatness-based control in particular, of robotic arms necessitate to compute the first and second time derivatives of the joint torques/forces required to achieve a desired motion. In view of the required computational efficiency, recursive $O(n)$-algorithms were proposed to this end. Aiming at compact yet efficient formulations, a Lie group formulation was recently proposed, making use of body-fixed and hybrid representation of twists and wrenches. In this paper…

Faster CONGEST Approximation Algorithms for Maximum Weighted Independent Set in Sparse Graphs
Salwa Faour, Fabian Kuhn
https://arxiv.org/abs/2506.10845 htt…

Faster CONGEST Approximation Algorithms for Maximum Weighted Independent Set in Sparse Graphs
The maximum independent set problem is a classic optimization problem that has also been studied quite intensively in the distributed setting. While the problem is hard to approximate in general, there are good approximation algorithms known for several sparse graph families. In this paper, we consider deterministic distributed CONGEST algorithms for the weighted version of the problem in trees and graphs of bounded arboricity. For trees, we prove that the task of deterministically computing …

Algorithmic methods of finite discrete structures. Topological graph drawing (part III)
Sergey Kurapov, Maxim Davidovsky
https://arxiv.org/abs/2506.10936 h…

Algorithmic methods of finite discrete structures. Topological graph drawing (part III)
The manuscript considers mathematical models for creating a topological drawing of a graph based on the methods of G. Ringel's vertex rotation theory. An algorithm is presented for generating a topological drawing of a flat part of a graph based on the selection of a basis for the cycle subspace C(G) using the Monte Carlo method. A steepest descent method for constructing a topological drawing of a flat subgraph is described in the manuscript. The topological drawing of a graph is constructed u…

A Goemans-Williamson type algorithm for identifying subcohorts in clinical trials
Pratik Worah
https://arxiv.org/abs/2506.10879 https://

A Goemans-Williamson type algorithm for identifying subcohorts in clinical trials
We design an efficient algorithm that outputs a linear classifier for identifying homogeneous subsets (equivalently subcohorts) from large inhomogeneous datasets. Our theoretical contribution is a rounding technique, similar to that of Goemans and Williamson (1994), that approximates the optimal solution of the underlying optimization problem within a factor of $0.82$. As an application, we use our algorithm to design a simple test that can identify homogeneous subcohorts of patients, that are …

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Junliang Xing, Yifan Zhang, Jian Cheng
https://arxiv.org/abs/2506.09404

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Combinatorial optimization problems are notoriously challenging due to their discrete structure and exponentially large solution space. Recent advances in deep reinforcement learning (DRL) have enabled the learning heuristics directly from data. However, DRL methods often suffer from limited exploration and susceptibility to local optima. On the other hand, evolutionary algorithms such as Genetic Algorithms (GAs) exhibit strong global exploration capabilities but are typically sample inefficien…

“Head of Algorithms”
What a job title. 🧙✨😁