Search · four archives
Search · four archives
10 papers · ranked by Valyu relevance
Peter Steffen, Robert Giegerich
Background Dynamic programming is a widely used programming technique in bioinformatics. In sharp contrast to the simplicity of textbook examples, implementing a dynamic programming algorithm for a novel and non-trivial application is a tedious and error prone task. The algebraic dynamic programming approach seeks to…
Christian Höner zu Siederdissen, Sonja J Prohaska, Peter F Stadler
Background Dynamic programming algorithms provide exact solutions to many problems in computational biology, such as sequence alignment, RNA folding, hidden Markov models (HMMs), and scoring of phylogenetic trees. Structurally analogous algorithms compute optimal solutions, evaluate score distributions, and perform…
Cédric Saule, Robert Giegerich
Pareto optimization combines independent objectives by computing the Pareto front of its search space, defined as the set of all solutions for which no other candidate solution scores better under all objectives. This gives, in a precise sense, better information than an artificial amalgamation of different scores into…
Georg Sauthoff, Mathias Möhl, Stefan Janssen, Robert Giegerich
Motivation: Dynamic programming is ubiquitous in bioinformatics. Developing and implementing non-trivial dynamic programming algorithms is often error prone and tedious. Bellman’s GAP is a new programming system, designed to ease the development of bioinformatics tools based on the dynamic programming technique.…
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…
Jesse Hoey, Robert St‐Aubin, Alan J. Hu, Craig Boutilier
Recently, structured methods for solving factored Markov decisions processes (MDPs) with large state spaces have been proposed recently to allow dynamic programming to be applied without the need for complete state enumeration. We propose and examine a new value iteration algorithm for MDPs that uses algebraic decision…
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…
Thomas J. Sargent, John Stachurski
We introduce a framework that represents dynamic programs as families of policy operators acting on a partially ordered set. We provide an optimality theory based on high-level assumptions and show how applications across a broad spectrum of dynamic programming subfields fit into this framework. We apply the framework…
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…
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.…