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Learn to write fast, parallel Julia code using Dagger.jl. Explore various programming interfaces, build example programs, and gain insights into parallelism through logging and profiling capabilities.
Learn to create stellar documentation for your Julia codebase. Master core principles, write effective docstrings, and use Documenter.jl to generate and deploy professional-quality docs online.
Explore JuMP for flexible optimization modeling at SINTEF, focusing on hydrogen infrastructure and circular logistics. Learn about complementary packages enhancing JuMP's functionality.
Introducing a general framework for modeling flow problems over hyper graphs, with applications in network optimization, power flow, and wireless networks. Includes fast, parallelizable algorithm implementation in Julia.
Explore novel algorithms for geographic decomposition in large-scale power systems, addressing inefficiencies in inter-regional interconnections using JuMP and PowerSimulations.jl.
Explore Julia's QUBO.jl ecosystem for solving binary optimization problems, featuring solver interfaces, benchmarking tools, and quantum hardware integration for advanced optimization techniques.
Explore design paradigms and lessons from Pyomo, covering model composition, optimization workflows, and new ideas in algebraic modeling languages for efficient problem-solving.
Accelerate infinite-dimensional optimization with InfiniteExaModels.jl, leveraging repetitive patterns for efficient derivative evaluations on CPU & GPU.
Explore recent advancements and developments in JuMP, the Julia-based mathematical optimization package, over the past year.
Explore StarAlgebras.jl for computing with arbitrary bases, improving polynomial optimization through symmetry-adapted bases and better-conditioned alternatives to monomials.
Explore convex optimization modeling frameworks, comparing cvxpy, pyomo, and JuMP. Learn about common student challenges and how JuMP addresses them in undergraduate and graduate courses.
Explore LoReSIO.jl, a Julia package for solving semi-infinite optimization problems using JuMP. Learn efficient techniques for handling infinite constraints and solving complex optimization challenges.
Stochastic programming approach for optimal biocontrol strategies against mosquito-borne diseases, considering uncertain weather conditions and using JuMP and Julia for efficient intervention planning.
Explore libHSL, a powerful Fortran package collection for scientific computation, now accessible in Julia through HSL.jl and Ipopt.jl with pre-built binaries.
Explore a hybrid quantum branch-and-bound method for solving QUBO problems, combining quantum hardware strengths with global search capabilities. Implementation details and performance evaluation included.
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