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Manopt.jl
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  • About
  • How to...
    • Get Started: Optimize!
    • Use AD in Manopt
    • Record Values
    • Do constrained Optimization
    • Do Geodesic Regression
    • Use Bézier Curves
    • Compute a Second Order Difference
    • Do Stochastic Gradient Descent
    • Speed up! Using gradF!
    • Illustrate Jacobi Fields
  • Solvers
    • Introduction
    • Alternating Gradient Descent
    • Augmented Lagrangian Method
    • Chambolle-Pock
    • Conjugate gradient descent
    • Cyclic Proximal Point
    • Douglas–Rachford
    • Exact Penalty Method
    • Frank-Wolfe
    • Gradient Descent
    • Levenberg–Marquardt
    • Nelder–Mead
    • Particle Swarm Optimization
    • Primal-dual Riemannian semismooth Newton
    • Quasi-Newton
    • Stochastic Gradient Descent
    • Subgradient method
    • Steihaug-Toint TCG Method
    • Trust-Regions Solver
  • Examples
    • Robust PCA
    • Rayleigh quotient
    • Frank Wolfe for Riemannian Center of Mass
  • Plans
    • Specify a Solver
    • Problem
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    • Stepsize
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    • Debug Output
    • Recording values
  • Functions
    • Introduction
    • Bézier curves
    • Cost functions
    • Differentials
    • Adjoint Differentials
    • Gradients
    • Jacobi Fields
    • Proximal Maps
    • Specific Manifold Functions
  • Helpers
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    • Data
    • Error Measures
    • Exports
  • Contributing to Manopt.jl
  • Notation
  • Function Index
Version
  • Helpers
  • Error Measures
  • Error Measures
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Error Measures

Manopt.meanSquaredError — Function
meanSquaredError(M, p, q)

Compute the (mean) squared error between the two points p and q on the (power) manifold M.

source
Manopt.meanAverageError — Function
meanSquaredError(M,x,y)

Compute the (mean) squared error between the two points x and y on the PowerManifold manifold M.

source
« DataExports »

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