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Add DIIS.mli

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Anthony Scemama 2018-05-31 10:47:31 +02:00
parent 07ee916a28
commit 6d783999af

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(** Direct Inversion of the Iterative Subspace algorithm.
At each iteration, one has:
- {% $\mathbf{p}$ %}, a vector of parameters
- {% $\mathbf{e}$ %}, an approximate error vector
The DIIS approximate solution is given by
{% \begin{align*}
\mathbf{p}^{\text{DIIS}} & = \sum_i c_i (\mathbf{p}^f + \mathbf{e}_i) \\
& = \sum_i c_i \mathbf{p}^f + \sum_i c_i \mathbf{e}_i) \\
\end{align*} %}
where {% $\mathbf{p}^f$ %} is the exact solution, so one wants to minimize
the norm of the error vector imposing the constraint that $\sum_i c_i = 1$ with
a Langrange multiplier {% $\lambda$ %}.
{%
\begin{align*}
\mathcal{L} & = ||\sum_i c_i \mathbf{e}_i)||^2 - \lambda \left(\sum_i c_i - 1\right) \\
& = \sum_{ij} c_i c_j B_{ij} - \lambda \left(\sum_i c_i - 1\right)
\end{align*}
with {% $B_{ij} = \langle \mathbf{e}_i | \mathbf{e}_j \rangle$ %}.
Equating zero to the derivatives of {% $\mathcal{L}$ %} with respect to {% $c_i$ %} and {% $\lambda$ %} leads to
{% \begin{equation*}
\begin{bmatrix}
B_{11} & B_{12} & B_{13} & ... & B_{1m} & -1 \\
B_{21} & B_{22} & B_{23} & ... & B_{2m} & -1 \\
B_{31} & B_{32} & B_{33} & ... & B_{3m} & -1 \\
\vdots & \vdots & \vdots & \vdots & \ddots & \vdots \\
B_{m1} & B_{m2} & B_{m3} & ... & B_{mm} & -1 \\
1 & 1 & 1 & ... & 1 & 0
\end{bmatrix} \begin{bmatrix} c_1 \\ c_2 \\ c_3 \\ \vdots \\ c_m \\ \lambda \end{bmatrix}=
\begin{bmatrix} 0 \\ 0 \\ 0 \\ \vdots \\ 0 \\ 1 \end{bmatrix}
\end{equation*}
%}
The coefficients are then used to update {% $\mathbf{p}$ %} as
{% $$
\mathbf{p}_{m+1}=\sum_{i=1}^m c_i\mathbf{p}_i.
$$ %}
*)
type t
val make : unit -> t
(** Initialize DIIS *)
val append : p:Lacaml.D.Vec.t -> e:Lacaml.D.Vec.t -> t
(** Append a parameter vector [p] and the corresponding error vector [e]. *)
val next : t -> Lacaml.D.Vec.t
(** Returns a new parameter vector. *)