PICO: An Object-Oriented Framework for Branch and Bound

Eckstein, Jonathan; Hart, William E.; Phillips, Cynthia A.

doi:10.2172/771506

Cited by 28 publications

(1 citation statement)

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“…BroMAP was implemented in C++ We used the PICO‐library54 for the BnB framework. The PICO‐library provides the data structures and methods to create/delete nodes and to search the tree.…”

Section: Resultsmentioning

confidence: 99%

Rotamer optimization for protein design through MAP estimation and problem‐size reduction

et al. 2009

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The search for the global minimum energy conformation (GMEC) of protein side chains is an important computational challenge in protein structure prediction and design. Using rotamer models, the problem is formulated as a NP-hard optimization problem. Dead-end elimination (DEE) methods combined with systematic A* search (DEE/A*) has proven useful, but may not be strong enough as we attempt to solve protein design problems where a large number of similar rotamers is eligible and the network of interactions between residues is dense. In this work, we present an exact solution method, named BroMAP (branch-and-bound rotamer optimization using MAP estimation), for such protein design problems. The design goal of BroMAP is to be able to expand smaller search trees than conventional branch-and-bound methods while performing only a moderate amount of computation in each node, thereby reducing the total running time. To achieve that, BroMAP attempts reduction of the problem size within each node through DEE and elimination by lower bounds from approximate maximum-a-posteriori (MAP) estimation. The lower bounds are also exploited in branching and subproblem selection for fast discovery of strong upper bounds. Our computational results show that BroMAP tends to be faster than DEE/A* for large protein design cases. BroMAP also solved cases that were not solved by DEE/A* within the maximum allowed time, and did not incur significant disadvantage for cases where DEE/A* performed well. Therefore, BroMAP is particularly applicable to large protein design problems where DEE/A* struggles and can also substitute for DEE/A* in general GMEC search.

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Section: Resultsmentioning

confidence: 99%

Rotamer optimization for protein design through MAP estimation and problem‐size reduction

et al. 2009

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A Multithreaded Branch‐and‐Bound Algorithm for Solving The Flow‐Shop Problem on A Multicore Environment

Mezmaz

Melab

Tuyttens

2013

Large Scale Network‐Centric Distributed Systems

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High‐level specifications for automatically generating parallel code

Acosta

Almeida

Peláez

2012

Concurrency and Computation

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SUMMARYThe arrival of multicore systems, along with the speed‐up potential available in graphics processing units, has given us unprecedented low‐cost computing power. These systems address some of the known architecture problems but at the expense of considerably increased programming complexity. Heterogeneity, at both the architectural and programming levels, poses a great challenge to programmers. Many proposals have been put forth to facilitate the job of programmers. Leaving aside proposals based on the development of new programming languages because of the effort this represents for the user (effort to learn and reuse code), the remaining proposals are based on transforming sequential code into parallel code, or on transforming parallel code designed for one architecture into parallel code designed for another. A different approach relies on the use of skeletons. The programmer has available set of parallel standards that comprise the basis for developing parallel code while programming sequential code. In this context, we propose a methodology for developing an automatic source‐to‐source transformation in a specific domain. This methodology is instantiated in a framework aimed at solving dynamic programming problems. Using this framework, the final user (a physician, mathematician, biologist, etc.) can express her problem using an equation in Latex, and the system will automatically generate the optimal parallel code for homogeneous or heterogeneous architectures. This approach allows for great portability toward these new emerging architectures and for great productivity, as evidenced by the computational results.Copyright © 2012 John Wiley & Sons, Ltd.

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PICO: An Object-Oriented Framework for Branch and Bound

Cited by 28 publications

References 16 publications

Rotamer optimization for protein design through MAP estimation and problem‐size reduction

Rotamer optimization for protein design through MAP estimation and problem‐size reduction

A Multithreaded Branch‐and‐Bound Algorithm for Solving The Flow‐Shop Problem on A Multicore Environment

High‐level specifications for automatically generating parallel code

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