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7 results for “Convex optimization”
Matlab data for paper ""Non-Asymptotic Rates for Communication Efficient Distributed Zeroth Order Strongly Convex Optimization" by Sahu et al.
<p>This is a Matlab data file for paper A.K. Sahu, D.Jakovetic, D. Bajovic and S. Kar, “Non-Asymptotic Rates for Communication Efficient Distributed Zeroth Order Strongly Convex Optimization”, IEEE Global Conference on Signal and Information Processing(GlobalSIP), 2018, see also: <a href="https://zenodo.org/record/3333576#.XSibRegzZPY">https://zenodo.org/record/3333576#.XSibRegzZPY</a></p>
A Convex Hull Formulation for the Design of Optimal Mixtures
<p>The files contain some of the formulations implemented in GAMS for this publication. All problems are solved in GAMS version 24.2.3 and are run on a single core of a dual 6 core Intel Xeon X5675 machine at 3.07 GHz.</p> <p>Supplementary files can be found at: <em>https://zenodo.org/record/35234</em></p>
Dataset for "Convex modeling of pumps in order to optimize their energy use" article
<p>This is the data set about pump optimization used in the submitted article "Convex modeling of pumps in order to optimize their energy use" . The proposed optimization method is shown, and also the two methods to which it is compared to.</p> <p> </p>
Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach
<p># Emission-Aware Optimization of Gas Networks</p> <p> </p> <p>This repository collects the Belgium gas network dataset, details on the training procedure, and codes to replicate the results reported in the following paper:</p> <p> </p> <p>*Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach *</p> <p> </p> <p>accepted for presentation at the 62nd IEEE Conference on Decision and Control, Dec. 13-15, 2023, Singapore.</p> <p> </p> <p>Materials are released with the Attribution 4.0 International (CC BY 4.0) license.</p> <p> </p> <p>The repository contains two folders:</p> <p>* ```operation_planning``` folder containing data and codes for neural network training and operation planning optimization </p> <p>* ```long_term_planning``` folder containing data and codes for neural network training and long-term planning optimization </p> <p> </p> <p>The models are implemented in ```Julia-1.6``` Language, using ```JuMP.jl``` using ```Flux.jl``` library for machine learning and JuMP.jl library for mathematical programming. Before running the code, make sure to activate the virtual environment from ```Project.toml``` files stored in each folder, e.g., by running </p> <p>```</p> <p>julia> ]</p> <p>(@v1.6) pkg> activate .</p> <p>(operation_planning) pkg> instantiate</p> <p>```</p> <p>For experiment settings, refer to ```exp_settings``` dictionary in file ```main.jl```. For network data, refer to ```.../data/case_BE```. </p> <p> </p> <p> </p>
Angiosperms follow a convex trade-off to optimize hydraulic safety and efficiency
<p><span>Intervessel pits are considered to function as valves that avoid embolism spreading and optimize efficient transport of xylem sap across neighbouring vessels. Hydraulic transport between vessels would therefore follow a safety-efficiency trade-off, which is directly related to the total intervessel pit area (A<sub>p</sub>), inversely related to the pit membrane thickness (T<sub>PM</sub>), and driven by a pressure difference.</span></p> <p><span>To test this hypothesis, we modelled the relative transport rate of gas (k<sub>a</sub>) and water (Q) at the intervessel pit level for 23 angiosperm species, and correlated these parameters with the water potential at which 50% of embolism occurs (</span><span>Ψ</span><sub><span>50</span></sub><span>). We also measured k<sub>a</sub> for 10 species using pneumatic measurements.</span></p> <p><span>The pressure difference across adjacent vessels, and estimated values of k<sub>a </sub>and Q were related to </span><span>Ψ</span><sub><span>50</span></sub><span>, following a convex safety-efficiency trade-off based on modelled and experimental data. Minor changes in T<sub>PM</sub> and A<sub>p </sub>exponentially affected the pressure difference and flow, respectively. </span></p> <p><span>Our results provide clear evidence that a xylem safety-efficiency trade-off is not linear, but convex due to flow across intervessel pit membranes, which represent mesoporous media within microporous conduits. Moreover, the convex nature of long-distance xylem transport may contribute to an adjustable fluid balance of plants, depending on environmental conditions. </span></p>
Angiosperms follow a convex trade-off to optimize hydraulic safety and efficiency
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Optimization of Gang-operated On-load Tap Changers in Multi-conductor Radial Networks: Formulation and Convex Relaxation
<p>DSS files for case studies in CIGRE conference paper</p> <p>Only the secondary of the regulator can tap between 0.9 and 1.1, this is not specified in the DSS files themselves</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.