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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.
Explore JuMP's optimization capabilities for time series modeling, enhancing SARIMA, Score Driven, and State Space Models with improved performance over traditional methods.
Flexible energy system modelling framework for operational and planning studies, featuring adaptable structures, uncertainty representation, and model decomposition for complex multi-energy systems.
Optimize supply chain matching with JuMP: Efficiently model connections in transportation networks, balancing idle times, delays, and resource allocation for improved performance and robustness.
Explore cutting-edge nonlinear optimization solvers in JuliaSmoothOptimizers. Learn about new packages, performance enhancements, and flexible implementations for large-scale problems using high-level linear algebra and automatic differentiation.
Explore EAGO.jl, an open-source global optimizer for MINLPs in Julia, and its integration with JuMP for enhanced accessibility and capabilities in solving complex optimization problems.
Explore the enhanced DisjunctiveProgramming.jl for modeling Generalized Disjunctive Programs, featuring improved performance, expanded constraint support, and compatibility with JuMP and InfiniteOpt.
Explore differentiating parametric JuMP models, combining DiffOpt and ParametricOptInterface for efficient optimization and parameter sensitivity analysis in Julia.
Accelerating optimization with ML approximations using JuMP. Learn to enhance efficiency in decision-making by exploiting similarities in parametric problems and integrating ML models with optimization techniques.
Explore piecewise affine convex approximations for optimization models using PiecewiseAffineApprox.jl, featuring flexible options and methods for best-fit calculations in Julia.
Learn to design model predictive controllers using ModelPredictiveControl.jl, an open-source package for advanced process control based on JuMP, featuring nonlinear state estimation and visualization tools.
Optimization model for electricity markets and energy sectors using graph theory and flexible time resolution, enhancing adaptability and efficiency in large-scale energy system modeling.
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