Awesome

HAPOD - Hierarchical Approximate Proper Orthogonal Decomposition

HAPOD - Hierarchical Approximate POD
version: 3.2 (2021-05-05)
by: C. Himpe (0000-0003-2194-6754), S. Rave (0000-0003-0439-7212)
under: BSD 2-Clause License (opensource.org/licenses/BSD-2-Clause)
summary: Fast distributed or incremental POD computation.

About

The HAPOD is an algorithm to compute the POD (left singular vectors, and singular values of a matrix) hierarchically for (column-wise partitioned) large-scale matrices, allowing to balance accuracy with performance. As a POD-of-PODs method, the HAPOD can be parallelized and further accelerated by user supplied SVD implementations.

Scope

Proper Orthogonal Decomposition (POD)
Singular Value Decomposition (SVD)
Principal Axis Transformation (PAT)
Principal Component Analysis (PCA)
Empirical Orthogonal Functions (EOF)
Empirical Eigenfunctions (EEF)
Karhunen-Loeve Transformation (KLT)

Applications

Dimension Reduction
Model Reduction
Low-Rank Approximation
Data Compression
Unsupervised Learning

Features

Error-driven
Rigorous bounds
Single pass (each data vector is needed only once)
Column-wise data partitions (inducing parallelizability)
Custom SVD backends

Functionality

Standard POD
Incremental HAPOD -> for memory-limited environments, e.g. single-board-computers
Distributed HAPOD -> for distributd memory environments, e.g. super-computers
Distributed-of-Incremental HAPOD

Algorithm

C. Himpe, T. Leibner, S. Rave: "Hierarchical Approximate Proper Orthogonal Decomposition"; SIAM Journal on Scientific Computing, 40(5): A3267--A3292, 2018.

Compatibility

GNU Octave >= 4.0
Mathworks MATLAB >= 2013b

Basic Usage

[svec,sval,meta] = hapod(data,bound,topo,relax,meta,depth,mysvd)

Arguments

data {cell} - snapshot data set, partitioned by column (blocks)
bound {scalar} - mean L_2 projection error bound
topo {string} - tree topology (see Topology)
relax {scalar} - relaxation parameter in (0,1) (see Relaxation)
depth {scalar} - total number of levels in tree (only required for incr_1)
meta {struct} - meta information structure (see Meta-Information)
mysvd {handle} - custom SVD backend (see Custom SVD)

Return Values

svec {matrix} POD modes (column vectors)
sval {vector} Singular values (column vector)
meta {struct} Meta-information structure

Topology

The HAPOD is computed based on a tree topology topo, with the data partitions at the tree's leafs. The following topologies are available:

'none' Standard POD
'incr' Incremental HAPOD (Complete)
'incr_1' Incremental HAPOD (Child nodes)
'incr_r' Incremental HAPOD (Root node)
'dist' Distributed HAPOD (Complete)
'dist_1' Distributed HAPOD (Child nodes)
'dist_r' Distributed HAPOD (Root node)

If all data partitions can be passed as the data argument, the types: none (standard POD), incr(emental) HAPOD or dist(ributed) HAPOD are applicable. In case only a single partition is passed at a time, the types: incr_1 and dist_1 should be used for the child nodes of the associated HAPOD tree, while the types: incr_r and dist_r should be used for the root nodes. The returned meta-information structure (or a cell-array thereof) has to be passed to the parent node in the associated HAPOD tree.

Relaxation

The relaxation parameter w (0 < w < 1) balances accuracy versus speed. Larger w near one means be more accurate, while w near zero means faster computation. The default value is w = 0.5.

Meta-Information

The meta structure contains the following meta-information of the completed sub-tree:

nSnapshots - Number of data columns passed to this HAPOD and its children.
nModes - Number of intermediate modes.
tNode - Computational time at this HAPOD's branch.

The argument meta only needs to be passed for topology types incr_r, dist_r and incr_1, unless it is first leaf. Especially, this means the user never has to create such a structure, since if it is required it is given as a previous HAPOD's return value.

Custom SVD

Via the mysvd argument a custom SVD function can be provided via a function handle with the following signature:

[U,d] = mysvd(X)

for a data matrix X, and returning left singular vectors in matrix U and singular values in column vector d. By default (or mysvd = eco) a standard rank-revealing SVD is used. Additionally, by mysvd = mos the method of snapshots can be selected.

Getting Started

Run the sample code:

RUNME()

which demonstrates the different implemented HAPOD variants and can be used as a template.

MapReduce

The distributed HAPOD is well suited for the MapReduce big data processing model. A basic MapReduce wrapper using the MATLAB (>=2020b) mapreduce function is provided by:

MAPRED()

Cite As

C. Himpe, T. Leibner and S. Rave: "Hierarchical Approximate Proper Orthogonal Decomposition"; SIAM Journal on Scientific Computing, 40(5): A3267--A3292, 2018.

Used In

P. Benner, C. Himpe: "Cross-Gramian-Based Dominant Subspaces"; Advances in Computational Mathematics, 45(5): 2533--2553, 2019.
B.J. Beach: "An Implementation-Based Exploration of HAPOD: Hierarchical Approximate Proper Orthogonal Decomposition"; Virgina Tech, Master Thesis, 2018.
C. Himpe, T. Leibner, S. Rave, J. Saak: "Fast Low-Rank Empirical Cross Gramians"; Proceedings in Applied Mathematics and Mechanics, 17: 841--842, 2017.