Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
Max A. Little, Xi He, Ugur Kayas
Dynamic programming (DP) is a broadly applicable algorithmic design paradigm for the efficient, exact solution of otherwise intractable, combinatorial problems. However, the design of such algorithms is often presented informally in an ad-hoc manner, and as a result is often difficult to apply correctly. In this paper…
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…
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…
Muhammad Maaz, Adam Strzeboński
We consider a generalization of polynomial programs: algebraic programs, which are optimization or feasibility problems with algebraic objectives or constraints. Algebraic functions are defined as zeros of multivariate polynomials. They are a rich set of functions that includes polynomials themselves, but also ratios…
Zedong Peng, Albert Lee, David E. Bernal Neira
Discrete-Steepest Descent Algorithm Authors: ['Zedong Peng' 'Albert Lee' 'David E. Bernal Neira'] Abstract— Dynamic optimization problems involving discrete decisions have several applications, yet lead to challenging optimization problems that must be addressed efficiently. Combining discrete variables with…
Timothy C. Y. Chan, Muhammad Maaz
geometry Authors: ['Timothy C. Y. Chan' 'Muhammad Maaz'] We introduce a new approach for deterministic sensitivity analysis of Markov reward processes, commonly used in cost-effectiveness analyses, via reformulation into a polynomial system. Our approach leverages cylindrical algebraic decomposition (CAD), a technique…
Mokhaled N. A. Al-Hamadani, Mohammed A. Fadhel, Laith Alzubaidi, Harangi Balazs + 2 more
'Harangi Balazs' 'Antonio Fernández-Caballero' 'Dominique Gruyer'] Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential…
Authors not listed
The SCF part of the HF-SCF method is responsible for finding the ground state as the global minimum of the one-determinant approximation of the electronic energy, which is a 4th order multivariable polynomial of the LCAO coefficients and Lagrange multipliers. In this work we replace this SCF part with algebraic…
Marcel Moosbrugger, Ezio Bartocci, Joost-Pieter Katoen, Laura Kovács
We describe the Amber tool for proving and refuting the termination of a class of probabilistic while-programs with polynomial arithmetic, in a fully automated manner. Amber combines martingale theory with properties of asymptotic bounding functions and implements relaxed versions of existing probabilistic termination…
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…
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…
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…
Kangbien Park
Humans have long employed directed evolution (DE) to engineer desired biological traits. In this paper, I introduce an algebraic framework that provides a quantitative representation of the general phenotypic traits of asexual populations, enabling the systematic modeling of DE processes. Within this framework, key…
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.…
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…
Thomas Krabichler, Josef Teichmann
The extensive application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset-Liability-Management (“Deep ALM”) for a technological transformation in the management of assets and liabilities along a whole term…
David P. Morton, Oscar Dowson, Bernardo K. Pagnoncelli
We study a class of multi-stage stochastic programs, which incorporate modeling features from Markov decision processes (MDPs). This class includes structured MDPs with continuous action and state spaces. We extend policy graphs to include decision-dependent uncertainty for one-step transition probabilities as well as…
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.…
Jing Xie, Qi Duan
Biological pathway analysis often requires identifying interventions that block reachability to an undesirable state, such as a disease-associated module, toxic byproduct, or adverse phenotype, while preserving reachability among essential biological functions. Motivated by this setting, we study the Reachability…
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…
Barouch Matzliach, Irad Ben-Gal, Evgeny Kagan, Gholamreza Anbarjafari
'Gholamreza Anbarjafari'] This paper addresses the problem of detecting multiple static and mobile targets by an autonomous mobile agent acting under uncertainty. It is assumed that the agent is able to detect targets at different distances and that the detection includes errors of the first and second types. The goal…