MatCUTEst is a package for facilitating the usage of CUTEst (https://github.com/ralna/CUTEst) in MATLAB on Linux.
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Updated
Jun 14, 2024 - MATLAB
MatCUTEst is a package for facilitating the usage of CUTEst (https://github.com/ralna/CUTEst) in MATLAB on Linux.
Computing approximate optimal designs for multivariate polynomial regressions
The source codes of this algorithm are also publicly available at https://aliasgharheidari.com/INFO.html. This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems.
The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, an…
Visit: https://aliasgharheidari.com/HGS.html. HGS optimizer is a population-based method with stochastic switching elements that enrich its main exploratory and exploitative behaviors and flexibility of HGS in dealing with challenging problem landscapes. The algorithm has been compared to LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPED…
Using Sum-of-squares hierarchies to solve the Total Variation norm minimization
FlexBox is a fexible MATLAB toolbox for finite dimensional convex variational problems in image processing and beyond.
Quantitative Magnetization Transfer MRI Optimization Toolbox
Matlab Simulation for T. K. Vu, M. Bennis, S. Samarakoon, M. Debbah and M. Latva-aho, "Joint In-Band Backhauling and Interference Mitigation in 5G Heterogeneous Networks," European Wireless 2016; 22th European Wireless Conference, Oulu, Finland, 2016, pp. 1-6. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7499273&isnumber=7499250
Heuristics based Water Pinch Analysis Toolbox for carrying out Water Minimization in Process Industry
Helper functions that interface with YALMIP, with a focus on polynomial optimization problems.
Numerical Optimization using "hill climbing" (aka Gradient Ascent)
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