24 papers · ranked by Valyu relevance
Thomas J. Sargent, John Stachurski
| Preface | | | viii | | --- | --- | --- | --- | | Common Symbols | | | xi | | | Common Abbreviations | | xii | | 1 | Introduction | | 1 | | | 1.1 Bellman Equations | | 3 | | | 1.1.1 | Finite-Horizon Job Search | 3 | | | 1.1.2 Infinite Horizon | | 10 | | | 1.2 Stability and Contractions | | 12 | | | 1.2.1 Vector Space…
David H. Lee, Aditya Prasad, Ramiro Deo-Campo Vuong, Tianyu Wang + 2 more
'Eric Han' 'David Kempe'] Dynamic programming (DP) is a fundamental and powerful algorithmic paradigm taught in most undergraduate (and many graduate) algorithms classes. DP problems are challenging for many computer science students because they require identifying unique problem structures and a refined understanding…
Ryo Kuroiwa, J. Christopher Beck
For combinatorial optimization problems, model-based approaches such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the 'holy grail' of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a new model-based paradigm…
Mateusz Gruzewski, Marek Palkowski, Ramon Antonio Rodriges Zalipynis
In this article, we present an efficient and concise OpenMP implementation of the Nussinov RNA folding algorithm, a well-known representative of non-serial polyadic dynamic programming (NPDP). Our goal is to develop an optimized implementation that can serve as a template for related dynamic programming applications.…
Ryo Kuroiwa, J. Christopher Beck
- We propose domain-independent dynamic programming (DIDP), a novel model-based paradigm for combinatorial optimization. - The modeling language for DIDP is designed so that a user can investigate efficient models by incorporating redundant information. - We implement DIDP solvers using heuristic search in an…
Monika Henzinger, Stefan Neumann, Harald Räcke, Stefan Schmid
Dynamic programming (DP) is one of the fundamental paradigms in algorithm design. However, many DP algorithms have to fill in large DP tables, represented by two-dimensional arrays, which causes at least quadratic running times and space usages. This has led to the development of improved algorithms for special cases…
Peter N. Loxley, Ka-Wai Cheung, Hector Zenil
An informative measurement is the most efficient way to gain information about an unknown state. We present a first-principles derivation of a general-purpose dynamic programming algorithm that returns an optimal sequence of informative measurements by sequentially maximizing the entropy of possible measurement…
Reynaldo Villarreal, Sindy Chamorro-Solano, Yolanda Vega-Sampayo, Carlos Alejandro Espejo + 9 more
'Carlos Alejandro Espejo' 'Steffen Cantillo' 'Luis Gaviria' 'Jheifer Paez' 'Carlos Ochoa' 'Silvia Moreno' 'Claudet Polo' 'Roberto Pestana-Nobles' 'Camilo Montoya' 'Jiawei Xiang'] Electrical power systems are crucial, yet vulnerable, due to their complex and interconnected nature, necessitating effective fault detection…
Fereshteh Vaezi Jezeie, Seyed Jafar Sadjadi, Ahmad Makui, Seyedali Mirjalili
'Seyedali Mirjalili'] Portfolio optimization is one of the most important issues in financial markets. In this regard, the more realistic are assumptions and conditions of modelling to portfolio optimization into financial markets, the more reliable results will be obtained. This paper studies the knapsack-based…
Anurag Dutta, K. Lakshmanan, John Harshith, Aditya Ramamoorthy
Problem: The Maximal Stretch Authors: ['Anurag Dutta' 'K. Lakshmanan' 'John Harshith' 'Aditya Ramamoorthy'] Abstract. Mathematical Selection is a method in which we select a particular choice from a set of such. It have always been an interesting field of study for mathematicians. Combinatorial optimisation is the…
Haosen Qin, Zhen Yu, Tailu Li, Xueliang Liu + 4 more
'Richard A. Lord' 'Jianlin Ren' 'Xiangfei Kong'] Finding the optimal balance between end-user’s comfort, lifestyle preferences and the cost of the heating, ventilation and air conditioning (HVAC) system, which requires intelligent decision making and control. This paper proposes a heating control method for HVAC based…
Henri Schmidt, Yuanyuan Qi, Benjamin J Raphael, Mohammed El-Kebir
We provide a meta algorithm for solving the tree separable dual problem using tree structured dual dynamic programming (TSDDP). We describe the algorithm’s basic ingredients and several properties that are conserved across different choices of loss function. To start, let $J_{i}$ be the optimal solution to the PPR…
Arseny Shur, Ido Tziony, Yaron Orenstein
Minimizers are sampling schemes which are ubiquitous in almost any high-throughput sequencing analysis. Assuming a fixed alphabet of size σ, a minimizer is defined by two positive integers k, w and a linear order ρ on k-mers. A sequence is processed by a sliding window algorithm that chooses in each window of length w…
Henri Schmidt, Yuanyuan Qi, Benjamin J. Raphael, Mohammed El-Kebir
Reconstructing the evolutionary history of tumors from bulk DNA sequencing of multiple tissue samples remains a challenging computational problem, requiring simultaneous deconvolution of the tumor tissue and inference of its evolutionary history. Recently, phylogenetic reconstruction methods have made significant…
Chee-Khian Sim
polynomial complexity Authors: ['Chee-Khian Sim'] In this note, we polynomially reduce an instance of the partition problem to a dynamic lot sizing problem, and show that solving the latter problem solves the former problem. By solving the dynamic programming formulation of the dynamic lot sizing problem, we show that…
Enrico Seiler, Myrthe Willemsen, Vitor C. Piro, Knut Reinert
A continued decrease in sequencing costs has facilitated the exponential increase in available sequencing data, with public databases like the European Nucleotide Archive (ENA) and Sequence Read Archive (SRA) reaching well in the order of petabases [1, 2]. This has been the incentive to develop more scalable tools for…
Jonathan Ferrer-Mestres, Thomas G. Dietterich, Olivier Buffet, Iadine Chadès
In conservation of biodiversity, natural resource management and behavioural ecology, stochastic dynamic programming, and its mathematical framework, Markov decision processes (MDPs), are used to inform sequential decision-making under uncertainty. Models and solutions of Markov decision problems should be…
Haojing Shao, Jue Ruan
Increasing the accuracy of the nucleotide sequence alignment is an essential issue in genomics research. Although classic dynamic-programming algorithms (e.g., Smith-Waterman and Needleman–Wunsch) guarantee to produce the optimal result, their time complexity hinders the application of large-scale sequence alignment.…
Ragnar Groot Koerkamp
We introduce APA2, an exact global pairwise aligner with respect to edit distance. The goal of APA2 is to unify the near-linear runtime of APA on similar sequences with the efficiency of dynamic programming (DP) based methods. Like Edlib, APA2 uses Ukkonen’s band doubling in combination with Myers’ bitpacking. APA2 1)…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Jorge Avila, Paola Bonizzoni, Simone Ciccolella, Gianluca Della Vedova + 4 more
The transition towards graph pangenomes is posing several new challenging questions, most notably how to extend the classical notion of read alignment from a sequence-to-sequence to a sequence-to-graph setting. Especially on variation graphs, where paths corresponding to individual genomes are labeled, notions of…
Authors not listed
High-precision dynamic simulations of hypersonic flows are crucial for hightemperature aerodynamics, particularly in addressing non-equilibrium effects in turbulent flows. The quasi-classical trajectory (QCT) method, based on microscopic molecular collisions, is a key approach to tackle this challenge. By generating…
Elena Zamaraeva, Christopher M. Collins, Dmytro Antypov, Vladimir V. Gusev + 6 more
Crystal Structure Prediction (CSP) is a fundamental computational problem in materials science. Basin-hopping is a prominent CSP method that combines global Monte Carlo sampling to search over candidate trial structures with local energy minimisation of these candidates. The sampling uses a stochastic policy to…
Authors not listed
Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according to which the synthesis operations are executed. In this work, we study the…