63 research outputs found
Automatic Differentiation Tools in Optimization Software
We discuss the role of automatic differentiation tools in optimization
software. We emphasize issues that are important to large-scale optimization
and that have proved useful in the installation of nonlinear solvers in the
NEOS Server. Our discussion centers on the computation of the gradient and
Hessian matrix for partially separable functions and shows that the gradient
and Hessian matrix can be computed with guaranteed bounds in time and memory
requirementsComment: 11 page
Optimality Measures for Performance Profiles
We examine the influence of optimality measures on the benchmarking process, and show that scaling requirements lead to a convergence test for nonlinearly constrained solvers that uses a mixture..
Lidar-Based Relative Position Estimation and Tracking for Multi-Robot Systems
Relative positioning systems play a vital role in current multi-robot systems. We present a self-contained detection and tracking approach, where a robot estimates a distance (range) and an angle (bearing) to another robot using measurements extracted from the raw data provided by two laser range finders. We propose a method based on the detection of circular features with least-squares fitting and filtering out outliers using a map-based selection. We improve the estimate of the relative robot position and reduce its uncertainty by feeding measurements into a Kalman filter, resulting in an accurate tracking system. We evaluate the performance of the algorithm in a realistic indoor environment to demonstrate its robustness and reliability
Tigris: Architecture and Algorithms for 3D Perception in Point Clouds
Machine perception applications are increasingly moving toward manipulating
and processing 3D point cloud. This paper focuses on point cloud registration,
a key primitive of 3D data processing widely used in high-level tasks such as
odometry, simultaneous localization and mapping, and 3D reconstruction. As
these applications are routinely deployed in energy-constrained environments,
real-time and energy-efficient point cloud registration is critical.
We present Tigris, an algorithm-architecture co-designed system specialized
for point cloud registration. Through an extensive exploration of the
registration pipeline design space, we find that, while different design points
make vastly different trade-offs between accuracy and performance, KD-tree
search is a common performance bottleneck, and thus is an ideal candidate for
architectural specialization. While KD-tree search is inherently sequential, we
propose an acceleration-amenable data structure and search algorithm that
exposes different forms of parallelism of KD-tree search in the context of
point cloud registration. The co-designed accelerator systematically exploits
the parallelism while incorporating a set of architectural techniques that
further improve the accelerator efficiency. Overall, Tigris achieves
77.2 speedup and 7.4 power reduction in KD-tree search over an
RTX 2080 Ti GPU, which translates to a 41.7% registration performance
improvements and 3.0 power reduction.Comment: Published at MICRO-52 (52nd IEEE/ACM International Symposium on
Microarchitecture); Tiancheng Xu and Boyuan Tian are co-primary author
Reassessing global change research priorities in mediterranean terrestrial ecosystems : how far have we come and where do we go from here?
Aim: Mediterranean terrestrial ecosystems serve as reference laboratories for the investigation of global change because of their transitional climate, the high spatiotemporal variability of their environmental conditions, a rich and unique biodiversity and a wide range of socio-economic conditions. As scientific development and environmental pressures increase, it is increasingly necessary to evaluate recent progress and to challenge research priorities in the face of global change. - Location: Mediterranean terrestrial ecosystems. - Methods: This article revisits the research priorities proposed in a 1998 assessment. - Results: A new set of research priorities is proposed: (1) to establish the role of the landscape mosaic on fire-spread; (2) to further research the combined effect of different drivers on pest expansion; (3) to address the interaction between drivers of global change and recent forest management practices; (4) to obtain more realistic information on the impacts of global change and ecosystem services; (5) to assess forest mortality events associated with climatic extremes; (6) to focus global change research on identifying and managing vulnerable areas; (7) to use the functional traits concept to study resilience after disturbance; (8) to study the relationship between genotypic and phenotypic diversity as a source of forest resilience; (9) to understand the balance between C storage and water resources; (10) to analyse the interplay between landscape-scale processes and biodiversity conservation; (11) to refine models by including interactions between drivers and socio-economic contexts; (12) to understand forest-atmosphere feedbacks; (13) to represent key mechanisms linking plant hydraulics with landscape hydrology. - Main conclusions:(1) The interactive nature of different global change drivers remains poorly understood. (2) There is a critical need for the rapid development of regional- and global-scale models that are more tightly connected with large-scale experiments, data networks and management practice. (3) More attention should be directed to drought-related forest decline and the current relevance of historical land use
Global Methods For Nonlinear Complementarity Problems
Global methods for nonlinear complementarity problems formulate the problem as a system of nonsmooth nonlinear equations approach, or use continuation to trace a path defined by a smooth system of nonlinear equations. We formulate the nonlinear complementarity problem as a bound-constrained nonlinear least squares problem. Algorithms based on this formulation are applicable to general nonlinear complementarity problems, can be started from any nonnegative starting point, and each iteration only requires the solution of systems of linear equations. Convergence to a solution of the nonlinear complementarity problem is guaranteed under reasonable regularity assumptions. The converge rate is Q-linear, Q-superlinear, or Q-quadratic, depending on the tolerances used to solve the subproblems
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