13 papers · ranked by Valyu relevance
Alan R. Rogers
The Legofit statistical package uses genetic data to estimate parameters describing population history. Previous versions used computer simulations to estimate probabilities, an approach that limited both speed and accuracy. This article describes a new deterministic algorithm, which makes Legofit much more accurate…
Gengsheng L. Zeng
A restricted Boltzmann machine is a fully connected shallow neural network. It can be used to solve many challenging optimization problems. The Boltzmann machines are usually considered probability models. Probability models normally use nondeterministic algorithms to solve their parameters. The Hopfield network which…
Artur Czumaj, Peter Davies-Peck, Merav Parter
In this paper, we study the power and limitations of component-stable algorithms in the low-space model of massively parallel computation (MPC). Recently Ghaffari, Kuhn and Uitto (FOCS 2019) introduced the class of component-stable low-space MPC algorithms, which are, informally, those algorithms for which the outputs…
Martin Šošić, Mile Šikić
We present Edlib, an open-source C/C++ library for exact pairwise sequence alignment using edit distance. We compare Edlib to other libraries and show that it is the fastest while not lacking in functionality, and can also easily handle very large sequences. Being easy to use, flexible, fast and low on memory usage, we…
Susanne Albers, Maximilian Janke
Makespan minimization on identical machines is a fundamental problem in online scheduling. The goal is to assign a sequence of jobs to m identical parallel machines so as to minimize the maximum completion time of any job. Already in the 1960s, Graham showed that Greedy is $2-1/m$-competitive. The best deterministic…
Wolfram Barfuss
A dynamical systems perspective on multi-agent learning, based on the link between evolutionary game theory and reinforcement learning, provides an improved, qualitative understanding of the emerging collective learning dynamics. However, confusion exists with respect to how this dynamical systems account of…
Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng + 4 more
It remains fundamentally unclear how to reprogram complex evolving systems. Here, we introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information.…
Jesse Kreger, Natalia L. Komarova, Dominik Wodarz
Multiple infection (when a single cell can become super-infected with multiple copies of virus) can effect evolutionary processes in HIV infection, such as the generation and spread of different mutations. Synaptic transmission (where multiple copies of virus can be transferred during a single cell-to-cell interaction)…
Sander Borst, Daniel Dadush, Sophie Huiberts, Danish Kashaev
Explorable heap selection is the problem of selecting the nth smallest value in a binary heap. The key values can only be accessed by traversing through the underlying infinite binary tree, and the complexity of the algorithm is measured by the total distance traveled in the tree (each edge has unit cost). This problem…
Michał Ćwik, Jerzy Józefczyk
An uncertain version of the permutation flow-shop with unlimited buffers and the makespan as a criterion is considered. The investigated parametric uncertainty is represented by given interval-valued processing times. The maximum regret is used for the evaluation of uncertainty. Consequently, the minmax regret discrete…
James R. Riehl, Maxwell I. Zimmerman, Matthew F. Singh, Gregory R. Bowman + 1 more
Equilibria, or fixed points, play an important role in dynamical systems across various domains, yet finding them can be computationally challenging. Here, we show how to efficiently compute all equilibrium points of discrete-valued, discrete-time systems on sparse networks. Using graph partitioning, we recursively…
Peter L. Bartlett, Chris Junchi Li, Jingfeng Wu, Bin Yu
In the field of optimization, developing accelerated methods for solving minimax and fixed-point problems remains a fundamental challenge. This paper presents a novel family of dual accelerated algorithms that achieve optimal convergence rates for both minimax and fixed-point problems. By exploring new anchoring…
Tobias Røikjer, Asger Hobolth, Kasper Munch
Phase-type distributions model the time until absorption in continuous or discrete-time Markov chains on a finite state space. The multivariate phase-type distributions have diverse and important applications by modeling rewards accumulated at visited states. However, even moderately-sized state spaces make the…