Disciplined Convex Programming in R using Convex.jl

addConstraint | Add constraints to optimization problem |

cvx_optim | Solve optimization problem |

dot | Inner product |

dotsort | Inner product of two vectors after sorted |

entropy | sum(-x * log(x)) |

Expr | Create expressions to be used for optimization problem... |

geomean | Geometric mean of x and y |

huber | Huber loss |

J | Make a variable to be of Julia's awareness |

lambdamax | Largest eigenvalues of x |

lambdamin | Smallest eigenvalues of x |

logdet | Log of determinant of x |

logisticloss | log(1 + exp(x)) |

logsumexp | log(sum(exp(x))) |

matrixfrac | x^T P^-1 x |

maximum | Largest elements |

minimum | Smallest elements |

neg | Negative parts |

norm | p-norm of x |

nuclearnorm | Sum of singular values of x |

operatornorm | Largest singular value of x |

pos | Positive parts |

problem_creating | Create optimization problem |

property | Get properties of optimization problem |

quadform | x^T P x |

setup | Doing the setup for the package convexjlr |

square | Square of x |

sumlargest | Sum of the largest elements |

sumsmallest | Sum of the smallest elements |

sumsquares | Sum of squares of x |

tr | Trace of matrix |

value | Get values of expressions at optimizer |

variable_creating | Create variable for optimization problem |

vec | Vector representation |

vecdot | Inner product of vector representation of two matrices |

vecnorm | p-norm of vector representation of x |

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