1,266 research outputs found
PARALLEL ALGORITHMS FOR NONLINEAR PROGRAMMING AND APPLICATIONS IN PHARMACEUTICAL MANUFACTURING
Effective manufacturing of pharmaceuticals presents a number of challenging optimization problems due to complex distributed, time-independent models and the need to handle uncertainty. These challenges are multiplied when real-time solutions are required. The demand for fast solution of nonlinear optimization problems, coupled with the emergence of new concurrent computing architectures, drives the need for parallel algorithms to solve challenging NLP problems. The goal of this work is the development of parallel algorithms for nonlinear programming problems on different computing architectures, and the application of large-scale nonlinear programming on challenging problems in pharmaceutical manufacturing
Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction
Current bundle adjustment solvers such as the Levenberg-Marquardt (LM)
algorithm are limited by the bottleneck in solving the Reduced Camera System
(RCS) whose dimension is proportional to the camera number. When the problem is
scaled up, this step is neither efficient in computation nor manageable for a
single compute node. In this work, we propose a stochastic bundle adjustment
algorithm which seeks to decompose the RCS approximately inside the LM
iterations to improve the efficiency and scalability. It first reformulates the
quadratic programming problem of an LM iteration based on the clustering of the
visibility graph by introducing the equality constraints across clusters. Then,
we propose to relax it into a chance constrained problem and solve it through
sampled convex program. The relaxation is intended to eliminate the
interdependence between clusters embodied by the constraints, so that a large
RCS can be decomposed into independent linear sub-problems. Numerical
experiments on unordered Internet image sets and sequential SLAM image sets, as
well as distributed experiments on large-scale datasets, have demonstrated the
high efficiency and scalability of the proposed approach. Codes are released at
https://github.com/zlthinker/STBA.Comment: Accepted by ECCV 202
Sequential Wnt Agonist then Antagonist Treatment Accelerates Tissue Repair and Minimizes Fibrosis
Tissue fibrosis compromises organ function and occurs as a potential
long-term outcome in response to acute tissue injuries. Currently, lack of
mechanistic understanding prevents effective prevention and treatment of the
progression from acute injury to fibrosis. Here, we combined quantitative
experimental studies with a mouse kidney injury model and a computational
approach to determine how the physiological consequences are determined by the
severity of ischemia injury, and to identify how to manipulate Wnt signaling to
accelerate repair of ischemic tissue damage while minimizing fibrosis. The
study reveals that Wnt-mediated memory of prior injury contributes to fibrosis
progression, and ischemic preconditioning reduces the risk of death but
increases the risk of fibrosis. Furthermore, we validated the prediction that
sequential combination therapy of initial treatment with a Wnt agonist followed
by treatment with a Wnt antagonist can reduce both the risk of death and
fibrosis in response to acute injuries
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
A New Preconditioning Approachfor an Interior Point–Proximal Method of Multipliers for Linear and Convex Quadratic Programming
In this paper, we address the efficient numerical solution of linear and
quadratic programming problems, often of large scale. With this aim, we devise
an infeasible interior point method, blended with the proximal method of
multipliers, which in turn results in a primal-dual regularized interior point
method. Application of this method gives rise to a sequence of increasingly
ill-conditioned linear systems which cannot always be solved by factorization
methods, due to memory and CPU time restrictions. We propose a novel
preconditioning strategy which is based on a suitable sparsification of the
normal equations matrix in the linear case, and also constitutes the foundation
of a block-diagonal preconditioner to accelerate MINRES for linear systems
arising from the solution of general quadratic programming problems. Numerical
results for a range of test problems demonstrate the robustness of the proposed
preconditioning strategy, together with its ability to solve linear systems of
very large dimension
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