MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.
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An acausal modeling framework for automatically parallelized scientific machine learning (SciML) ...
Optimization Problems for Julia
A package for Multiple criteria decision-making techniques in Julia
Data Structures for Optimization Models
Julia's CUTEst Interface
Polyhedral Computation Interface
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
A Julia Basket of Hand-Picked Krylov Methods
Interface for defining discrete and continuous-space MDPs and POMDPs in python. Compatible with t...
A Julia/JuMP-based Global Optimization Solver for Non-convex Programs
A solver for nonlinear programming
Performance and data profiles
A Julia package for Cluster Validity Indices (CVIs).
Computing reachable states of dynamical systems in Julia
A Julia library of summation-by-parts (SBP) operators used in finite difference, Fourier pseudosp...