Solve a test problem using manually selected rules

The problem Upload file:

Or select matrix and solution vector below.

Matrix baart deriv2 foxgood gravity heat κ = i_laplace phillips shaw spikes t_max = wing gauss σ = hilbert lotkin moler α = pascal prolate w =		Solution baart deriv2 example = foxgood gravity heat i_laplace example = phillips shaw spikes t_max = wing t₁ = t₂ = constant linear quadratic sinusoidal linear+sinusoidal step function
Problem size:

Additional smoothness:
Normalize problem

Noise level

Right hand side

Noise distribution:
normal
uniform
Poisson λ =
normal with outlier

Noise level:
relative absolute
actual noise level standard deviation

Correlation coefficient:

Matrix

Noise distribution:
normal
uniform

Noise level:
relative absolute
actual noise level standard deviation

Number of noise vectors:
Random seed:

Information about noise

	Upper bounds δ = η =
	These are relative absolute noise levels.
	Standard deviation σ = σ_η =
	These are relative absolute noise levels.
	No information about noise is known.

Parameter choice rules

Delta-rules

Unstable delta-rules
Discrepancy principle (D)	c =
Monotone error rule (ME)	c =
ME rule with post-estimation (MEe) γ =	c =
Adjusted monotone error rule (MEa)
Raus-Gfrerer rule (RG)	c =
Stable delta-rules
Balancing principle (BP)	c =
Rule R1 k =	c =
Rule R2(q=2, k=2, l=1/2)	c =
Rule R2(q=2, k=1, l=1/2)	c =
A priori parameter selection
A priori (Apri) α =

Heuristic rules

Quasioptimality criterion (Q)	κ	Nb
Hanke-Raus rule (HR)	κ	Nb
Regińska's rule (Reg) τ =	κ	Nb
Modified Regińska's rule (MReg)	κ	Nb
Hybrid rule (Hyb)
Brezinski-Rodriguez-Seatzu rule (BRS)	κ	Nb
Generalized cross-validation (GCV)	κ	Nb
Robust generalized cross-validation (RGCV) γ =	κ	Nb
Strong robust generalized cross-validation (SRGCV) γ =	κ	Nb
Modified generalized cross-validation (MGCV) c =	κ	Nb
Generalized maximum likelihood method (GML)	κ	Nb
Residual method (Res)	κ	Nb
HR monotone error rule (HRME)	κ	Nb
HR(1/4) rule	κ	Nb

Technical Parameter interval: α_M = α₀ = stepsize q =