object LassoAdmm
The LassoAdmm class performs LASSO regression using Alternating Direction
Method of Multipliers (ADMM). Minimize the following objective function to
find an optimal solutions for 'x'.
argmin_x (1/2)||Ax − b||_2^2 + λ||x||_1
A = data matrix b = response vector λ = weighting on the l_1 penalty x = solution (coefficient vector)
- See also
euler.stat.yale.edu/~tba3/stat612/lectures/lec23/lecture23.pdf
https://web.stanford.edu/~boyd/papers/admm_distr_stats.html
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- def fast_sthresh(v: VectoD, thr: Double): VectoD
Return the fast soft thresholding function.
Return the fast soft thresholding function.
- v
the vector to threshold
- thr
the threshold
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- def reset: Unit
Reset the warm start map.
- def solve(a: MatrixD, b: VectoD, λ: Double = 0.01): VectoD
Solve for 'x' using ADMM.
Solve for 'x' using ADMM.
- a
the data matrix
- b
the response vector
- λ
the regularization l_1 penalty weight
- def solveCached(ata_ρI_inv: MatriD, atb: VectoD, λ: Double): VectoD
Solve for 'x' using ADMM using cached factorizations for efficiency.
Solve for 'x' using ADMM using cached factorizations for efficiency.
- ata_ρI_inv
cached (a.t * a + ρI)^-1
- atb
cached a.t * b
- λ
the regularization l_1 penalty weight
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- val ρ: Int
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