mp3: done 1-3

This commit is contained in:
Claudio Maggioni 2020-10-21 15:21:51 +02:00
parent 339c7910dd
commit f813322cbd
62 changed files with 82874 additions and 29 deletions

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@ -1,4 +1,18 @@
n = 5;
A = createA(5);
spy(A);
matlab2tikz('../ex2_2_spy.tex');
A = createA(100);
figure;
subplot(1,2,1)
spy(A);
subplot(1,2,2)
spy(chol(A));
matlab2tikz('../ex2_3_spy.tex');
function A = createA(n)
A = sparse(n, n);
A(1,:) = 1;
A(end,:) = 1;
@ -7,15 +21,7 @@ A(:,end) = 1;
% make sure diagonal is 0
A(1,1) = 0;
A(n,n) = 0;
A(end,end) = 0;
A = A + diag(n:(2*n-1));
spy(A);
matlab2tikz('../ex2_2_spy.tex');
figure;
subplot(1,2,1)
spy(A);
subplot(1,2,2)
spy(chol(A));
matlab2tikz('../ex2_3_spy.tex');
end

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@ -122,7 +122,7 @@ to solve $Ax = b$
for a given righthand-side vector would be problematic.}
Here is the plot of \texttt{spy(A)} (on the left) and \texttt{chol(spy(A))} (on
the right).
the right) for $n = 100$.
\centering{\input{ex2_3_spy.tex}}

15
mp3/Makefile Normal file
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filename=template
pdf:
pdflatex ${filename}
#bibtex ${filename}
pdflatex ${filename}
pdflatex ${filename}
make clean
read:
evince ${filename}.pdf &
clean:
rm -f *.out *.log *.bbl *.blg *.aux ${filename}.log ${filename}.ps ${filename}.aux ${filename}.out ${filename}.dvi ${filename}.bbl ${filename}.blg

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function [A,xyz] = grid3d(k)
% GRID3D : Generate 3-dimensional 7-point finite difference mesh.
%
% [A,xyz] = GRID3D(k) returns a k^3-by-k^3 symmetric positive definite
% matrix A with the structure of the k-by-k-by-k 7-point grid,
% and an array xyz of coordinates for the grid points.
a = blockdiags ([-1 6 -1], -1:1, k, k);
I = speye (k, k);
aa = blockdiags ([-I a -I], -1:1, k, k);
II = speye(k^2,k^2);
A = blockdiags ([-II aa -II], -1:1, k, k);
A = diag(diag(A)) - A;
xyz = zeros(k^3,3);
x = ones(k,1) * (1:k);
y = x';
j = 1;
for i = 1:k
xyz(j:j+k^2-1 , 1) = x(:);
xyz(j:j+k^2-1 , 2) = y(:);
xyz(j:j+k^2-1 , 3) = i * ones(k^2,1);
j = j + k^2;
end
end

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function [A,xyz] = grid3dt(k)
% GRID3DT : Generate 3-dimensional tetrahedral finite element mesh.
%
% [A,xyz] = GRID3DT(k) returns a k^3-by-k^3 symmetric positive definite
% matrix A of the k-by-k-by-k grid, with cells divided into tetrahedra,
% and an array xyz of coordinates for the grid points.
% 1-d mesh
a = blockdiags ([-1 14 -1], -1:1, k, k);
% glue to laminate 1-d meshes into 2-d meshes
b = blockdiags ([-1 -1], [0 1], k, k);
% 2-d mesh
aa = blockdiags ([b a b'], -1:1, k, k);
% glue to laminate 2-d meshes into 3-d meshes
bb = blockdiags ([b b'], [0 1], k, k);
% 3-d mesh
A = blockdiags ([bb aa bb'], -1:1, k, k);
A = diag(diag(A)) - A;
% xyz coordinates of nodes
xyz = zeros(k^3,3);
x = ones(k,1) * (1:k);
y = x';
j = 1;
for i = 1:k
slab = j:j+k^2-1;
xyz(slab,1) = x(:);
xyz(slab,2) = y(:);
xyz(slab,3) = i * ones(k^2,1);
j = j + k^2;
end
end

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function [A,xy] = grid5(k)
% GRID5 : Generate 5-point finite difference mesh.
%
% [A,xy] = GRID5(k) returns a k^2-by-k^2 symmetric positive definite
% matrix A with the structure of the k-by-k 5-point grid,
% and an array xy of coordinates for the grid points.
a = blockdiags ([-1 4 -1], -1:1, k, k);
I = speye (k, k);
A = blockdiags ([-I a -I], -1:1, k, k);
A = diag(diag(A)) - A;
xy = zeros(k^2,2);
x = ones(k,1) * (1:k);
y = x';
xy(:,1) = x(:);
xy(:,2) = y(:);
end

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function [A,xy] = grid5rec(k1, k2)
% GRID5lrec : Generate 5-point finite difference on a rectangular mesh.
%
% [A,xy] = GRID5REC(k) returns a k1*k2-by-k1*k2 symmetric positive definite
% matrix A with the structure of the k-by-k 5-point grid,
% and an array xy of coordinates for the grid points.
a = blockdiags ([-1 4 -1], -1:1, k1, k1);
I = speye (k1, k1);
A = blockdiags ([-I a -I], -1:1, k2, k2);
A = diag(diag(A)) - A;
[x,y] = meshgrid(1:k2, 1:k1);
xy = zeros(k1*k2,2);
xy(:,1) = x(:);
xy(:,2) = y(:);
end

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function [A,xy] = grid5recRotate(k1, k2, angle)
% GRID5lrec : Generate 5-point finite difference on a rectangular mesh.
%
% [A,xy] = GRID5REC(k) returns a k1*k2-by-k1*k2 symmetric positive definite
% matrix A with the structure of the k-by-k 5-point grid,
% and an array xy of coordinates for the grid points.
a = blockdiags ([-1 4 -1], -1:1, k1, k1);
I = speye (k1, k1);
A = blockdiags ([-I a -I], -1:1, k2, k2);
A = diag(diag(A)) - A;
[x,y] = meshgrid(1:k2, 1:k1);
rad = angle*pi/180;
newX = x * cos(rad) - y * sin(rad);
newY = y * cos(rad) + x * sin(rad);
xy = zeros(k1*k2,2);
xy(:,1) = newX(:);
xy(:,2) = newY(:);
end

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function [A,xy] = grid7(k)
% GRID7 : Generate 7-point finite difference mesh.
%
% [A,xy] = GRID7(k) returns a k^2-by-k^2 symmetric positive definite
% matrix A with the structure of the k-by-k 7-point grid,
% and an array xy of coordinates for the grid points.
a = blockdiags ([-1 6 -1], -1:1, k, k);
b = blockdiags ([-1 -1], [0 1], k, k);
A = blockdiags ([b a b'], -1:1, k, k);
A = diag(diag(A)) - A;
xy = zeros(k^2,2);
x = ones(k,1) * (1:k);
y = x';
xy(:,1) = x(:);
xy(:,2) = y(:);
end

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function [A,xy] = grid9(k)
% GRID9 : Generate 9-point finite difference mesh.
%
% [A,xy] = GRID9(k) returns a k^2-by-k^2 symmetric positive definite
% matrix A with the structure of the k-by-k 9-point grid,
% and an array xy of coordinates for the grid points.
a = blockdiags ([-4 20 -4], -1:1, k, k);
b = blockdiags ([-1 -4 -1], -1:1, k, k);
A = blockdiags ([b a b], -1:1, k, k);
A = diag(diag(A)) - A;
xy = zeros(k^2,2);
x = ones(k,1) * (1:k);
y = x';
xy(:,1) = x(:);
xy(:,2) = y(:);
end

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function [A,xy] = gridt(k)
% GRIDT : Generate triangular mesh.
%
% [A,xy] = GRIDT(k) returns a k*(k+1)/2-square symmetric positive
% definite matrix A with the structure of an equilateral triangular
% mesh with side k, and an array xy of coordinates of the mesh points.
% Start with a square 7-point grid.
[A,xy] = grid7(k);
% Mask off one triangle of it.
xy = xy-1;
f = find(xy(:,1)+xy(:,2)<k);
A = A(f,f);
xy = xy(f,:);
% Make the other triangle equilateral.
T = [ 1 0 ; 1/2 2/sqrt(5)];
xy = xy*T;
end

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function metismex
% METISMEX Establish an interface between METIS and Matlab
%
% [map,edgecut] = metismex('PartGraphRecursive',A,nparts,options);
% [map,edgecut] = metismex('PartGraphKway',A,nparts,options);
% [perm,iperm] = metismex('EdgeND',A,options);
% [perm,iperm] = metismex('NodeND',A,options);
%
% options is now a structure with the following fields:
% 'seed' : an integer for the random seed used in metis
% 'ctype' : 'rm' or 'shem' [default]
% 'iptype' : 'grow' or 'random' (only applys for recursive bisection)
% 'objtype' : 'cut' or 'vol' [default] (only applys for part kway)
% 'rtype' : '1sided' [default] or '2sided'
% 'ufactor' : an integer for balance
% 'pfactor' : an integer for minimum degree of vertex to be ordered last
% 'ccorder' : a flag (value ignored) to order connected components
% separately
% 'nseps' : an integer for the number of separators tried at each level
% (default 1)
% 'niter' : an integer for the number of refinement iterations
% (default 10)
% 'ncuts' : number of initial partitions to test (default 1)
% 'dbglvl' : the debug level (default 0)
%
% The output and options commands are optional.
%
% Note that error checking is not done: make sure A is structurally
% symmetric or it will crash.

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function [p1,p2] = metispart(A,xy);
% METISPART : Partition a graph using Metis default method.
%
% p = metispart(A) returns a list of the vertices on one side of
% a partition obtained by Metis 4.0 applied to the graph of A.
%
% Optional arguments:
% metispart(A,xy) draws a picture of the partitioned graph,
% using the rows of xy as vertex coordinates.
% [p1,p2] = metispart(...) also returns the list of vertices
% on the other side of the partition.
%
% See also METISMEX (which accepts all the Metis options),
% GEOPART, GSPART, SPECPART, METISDICE, METISND.
%
% John Gilbert 3 Jul 01
% Copyright (c) 1990-2001 by Xerox Corporation. All rights reserved.
% HELP COPYRIGHT for complete copyright and licensing notice.
if nargin < 2
xy = 0;
end;
picture = max(size(xy)) > 1;
map = metismex('PartGraphRecursive',A,2);
[p1,p2] = other(map);
if picture
gplotpart(A,xy,p1);
title('Metis Partition')
end;

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function [out1,out2] = other(in1,in2);
% OTHER : Find the other part of a partition, or
% convert a partition to the other representation.
%
% part2 = other(part1,n) : part1 is a list of subscripts in 1:n;
% part2 becomes the complementary list.
% part2 = other(part1,A) : Same, except n is taken as max(size(A)).
% [part2,p] = other(part1,n) : Also returns 0/1 partition vector of length n.
% [part2,p] = other(part1,A) : Same.
% [part1,part2] = other(p) : Converts 0/1 vector to lists of subscripts.
%
% John Gilbert, 1993.
% Copyright (c) 1990-1996 by Xerox Corporation. All rights reserved.
% HELP COPYRIGHT for complete copyright and licensing notice.
% Modified 3 Jul 01 by JRG to fix a bug in the "map" case
if nargin == 1
out1 = find(in1 == 0);
out2 = find(in1 ~= 0);
else
if max(size(in2)) > 1
in2 = max(size(in2));
end;
out2 = full(spones(sparse(1,in1,1,1,in2)));
out1 = find(out2 == 0);
end;

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function Bench_bisection()
% Compare various graph bisection algorithms
%
% D.P & O.S for Numerical Computing at USI
% add the necessaty paths
addpaths_GP;
warning('off','all');
picture = 1;
if nargin < 1
whichdemos = [1 2 3];
end;
format compact;
disp(' *********************************************')
disp(' *** Graph bisection benchmark ***');
disp(' *********************************************')
disp(' ');
disp(' The file "Toy_meshes.mat" contains sample meshes with coordinates.');
disp(' ');
% load meshes
load Toy_meshes;
whos;
for nmesh = 1:7
close all; clf reset;
if (nmesh==1)
disp(' ');
disp(' Function "grid5rec" produces a rectangular grid:');
disp(' ');
disp('[A,xy] = grid5rec(8,80);');
disp(' ');
[W,coords] = grid5rec(8, 80);
end
if (nmesh==2)
disp(' ');
disp(' Function "grid5rec" produces a rectangular grid:');
disp(' ');
disp('[A,xy] = grid5rec(80,8);');
disp(' ');
[W,coords] = grid5rec(80, 8);
end
if (nmesh==3)
disp(' ');
disp(' Function "grid5recRotate" produces a rotated rectangular grid:');
disp(' ');
disp('[A,xy] = grid5recRotate(80,8, -45);');
disp(' ');
[W,coords] = grid5recRotate(80, 8, -45);
end
if (nmesh==4)
disp(' ');
disp(' Function "gridt" produces a triangular grid:');
disp(' ');
disp(' (See also grid5, grid7, grid9, grid3d, grid3dt.)');
disp(' ');
disp('[A,xy] = gridt(40);');
disp(' ');
[W,coords] = gridt(20);
end
if (nmesh==5)
disp(' ');
disp(' Function "gridt" produces a triangular grid:');
disp(' ');
disp(' (See also grid5, grid7, grid9, grid3d, grid3dt.)');
disp(' ');
disp('[A,xy] = gridt(30);');
disp(' ');
[W,coords] = grid9(30);
end
if (nmesh==6)
W = Smallmesh;
coords = Smallmesh_coords;
end
if (nmesh==7)
W = Tapir;
coords = Tapir_coords;
end
if (nmesh==8)
W = Eppstein;
coords = Eppstein_coords;
end
disp(' ');
disp(' *********************************************')
disp(' *** Various Bisection Methods *** ');
disp(' *********************************************')
disp(' ');
disp(' ');
if (nmesh==1)
disp('An initial rectangular grid5rec(8,80) mesh');
end
if (nmesh==2)
disp('An initial rectangular grid5rec(80,8) mesh');
end
if (nmesh==3)
disp('An initial rectangular grid5rec(80,8) mesh, rotated by 45 degree');
end
if (nmesh==4)
disp(' gridt(20) mesh');
end
if (nmesh==5)
disp(' gridt9(20) mesh');
end
if (nmesh==6)
disp(' Small mesh ');
end
if (nmesh==7)
disp(' "Tapir" is a test of a no-obtuse-angles mesh generation algorithm');
disp(' due to Bern, Mitchell, and Ruppert. ');
end
if (nmesh==8)
disp(' Eppstein mesh');
end
figure(1)
disp('gplotg(Tmesh,Tmeshxy);');
disp(' ');
gplotg(W,coords);
nvtx = size(W,1);
nedge = (nnz(W)-nvtx)/2;
xlabel([int2str(nvtx) ' vertices, ' int2str(nedge) ' edges'],'visible','on');
disp(' Hit space to continue ...');
pause;
disp(' 1. Coordinate bisection of a mesh. ');
disp(' p = coordpart(A,xy) returns a list of the vertices ');
disp(' on one side of a partition obtained by bisection ');
disp(' perpendicular to a coordinate axis. We try every ');
disp(' coordinate axis and return the best cut. Input W is ');
disp(' the adjacency matrix of the mesh; each row of xy is ');
disp(' the coordinates of a point in d-space. ');
figure(2)
[p1,p2] = bisection_coordinate(W,coords,picture);
[cut_coord] = cutsize(W,p1);
disp('Space to continue ...');
pause;
figure(3)
disp(' ');
disp(' 2. A multilevel method from the "Metis 5.0.2" package.');
disp(' This will only work if you have Metis and its Matlab interface.');
disp(' ');
[p1,p2] = bisection_metis(W,coords,picture);
[cut_metis] = cutsize(W,p1);
disp(' ');
disp(' Hit space to continue ...');
pause;
disp(' ');
disp(' 3. Spectral partitioning, which uses the second eigenvector of');
disp(' the Laplacian matrix of the graph, also known as the "Fiedler vector".');
disp(' ');
figure(6)
[p1,p2] = bisection_spectral(W,coords,picture);
[cut_spectral] = cutsize(W,p1);
disp(' ');
disp(' Hit space to continue ...');
pause;
figure(7)
disp(' ');
disp(' 4. Inertial partitioning, which uses the coordinates to find');
disp(' a separating line in the plane.');
disp(' ');
[p1,p2] = bisection_inertial(W,coords,picture);
[cut_inertial] = cutsize(W,p1);
disp(' ');
disp(' Hit space to continue ...');
pause;
close all;
format;
disp(' ');
disp(' *****************************************************************')
disp(' *** Bisection Benchmark *** ');
disp(' *** D.P. & O.S. for Numerical Computing, USI Lugano *** ');
disp(' *****************************************************************')
disp(' ');
disp(' ');
end

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function [cut_recursive,cut_kway] = Bench_metis(picture)
% Compare recursive bisection and direct k-way partitioning,
% as implemented in the Metis 5.0.2 library.
% Add necessary paths
addpaths_GP;
% Graphs in question
% load 'airfoil1.mat' ;
% load 'crack.mat';
% Steps
% 1. Initialize the cases
% 2. Call metismex to
% a) Recursively partition the graphs in 16 and 32 subsets.
% b) Perform direct k-way partitioning of the graphs in 16 and 32 subsets.
% 3. Visualize the results for 32 partitions
end

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% Benchmark for recursively partitioning meshes, based on various
% bisection approaches
%
% D.P & O.S for Numerical Computing in USI
% add necessary paths
addpaths_GP;
nlevels_a = 3;
nlevels_b = 4;
fprintf(' *********************************************\n')
fprintf(' *** Recursive graph bisection benchmark ***\n');
fprintf(' *********************************************\n')
% load cases
cases = {
'airfoil1.mat';
'3elt.mat';
'barth4.mat';
'mesh3e1.mat';
'crack.mat';
};
nc = length(cases);
maxlen = 0;
for c = 1:nc
if length(cases{c}) > maxlen
maxlen = length(cases{c});
end
end
for c = 1:nc
fprintf('.');
sparse_matrices(c) = load(cases{c});
end
fprintf('\n\n Report Cases Nodes Edges\n');
fprintf(repmat('-', 1, 40));
fprintf('\n');
for c = 1:nc
spacers = repmat('.', 1, maxlen+3-length(cases{c}));
[params] = Initialize_case(sparse_matrices(c));
fprintf('%s %s %10d %10d\n', cases{c}, spacers,params.numberOfVertices,params.numberOfEdges);
end
%% Create results table
fprintf('\n%7s %16s %20s %16s %16s\n','Bisection','Spectral','Metis 5.0.2','Coordinate','Inertial');
fprintf('%10s %10d %6d %10d %6d %10d %6d %10d %6d\n','Partitions',8,16,8,16,8,16,8,16);
fprintf(repmat('-', 1, 100));
fprintf('\n');
for c = 1:nc
spacers = repmat('.', 1, maxlen+3-length(cases{c}));
fprintf('%s %s', cases{c}, spacers);
sparse_matrix = load(cases{c});
% Recursively bisect the loaded graphs in 8 and 16 subgraphs.
% Steps
% 1. Initialize the problem
[params] = Initialize_case(sparse_matrices(c));
W = params.Adj;
coords = params.coords;
% 2. Recursive routines
% i. Spectral
% ii. Metis
% iii. Coordinate
% iv. Inertial
% 3. Calculate number of cut edges
% 4. Visualize the partitioning result
fprintf('%6d %6d %10d %6d %10d %6d %10d %6d\n',0,0,...
0,0,0,0,0,0);
end

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% Graph Partitioning Toolbox.
%
% Datasets
% 2d_meshes - Graphs from aeronautics applications
% Mesh_generation - Simple 2d and 3d mesh generation routines
%
% External
% metismex - Interface between Metis 5.0.2 and Matlab
%
% Bisection methods.
% bisection_spectral - Spectral bisection
% bisection_coordinate - Coordinate bisection.
% bisection_inertial - Inertial bisection.
% bisection_metis - Multilevel method from Metis.
% partition - Partition points by a plane.
%
% Multiway partitions.
% rec_bisection - Use any bisection method to get a multiway partition.
%
%
% Benchmark scripts
% Bench_bisection - Compare various graph bisection algorithms
% Bench_eigen_plot - Visualize information from the eigenspectrum
% of the graph Laplacian
% Bench_metis_comparison - Compare recursive bisection and direct k-way partitioning,
% as implemented in the Metis 5.0.2 library.
% Bench_rec_bisection - Benchmark for recursively partitioning meshes, based on various
% bisection approaches
%
%
% Visualization and graphics.
% gplotpart - Draw a 2-way partition.
% gplotmap - Draw a multiway partition.
% highlight - Draw a mesh with some vertices highlighted.
% gplotg - Draw a 2D or 3D mesh (replaces Matlab's gplot).
% etreeplotg - Draw an elimination tree (replaces Matlab's etreeplot).
% spypart - Matrix spy plot with partition boundaries.
%
% Miscellaneous.
% cutsize - Find or count edges cut by a partition.
% other - Other side of a partition.
% adjacency_to_incidence - create the incidence matrix of the graph
% Contents - contents of the partitioning toolbox
% blockdiags - Create matrix with specified block diagonals.

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function [params] = Initialize_case(SM_case)
% Adjacency
params.Adj = SM_case.Problem.A;
% Incidence
params.Inc = adjacency_to_incidence(params.Adj);
% Edges
params.numberOfEdges = size(params.Inc,1);
% Vertices
params.numberOfVertices = size(params.Inc,2);
% coords (if exist)
if isfield(SM_case, 'Problem') && isfield(SM_case.Problem, 'aux') && ...
isfield(SM_case.Problem.aux, 'coord')
params.coords = SM_case.Problem.aux.coord;
end
end

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function handle = gplotg(A,xy,lc)
% GPLOTG : Plot a "graph theoretic" graph.
%
% handle = gplotg(A,xy) plots the graph specified by A and xy.
% A graph, G, is a set of nodes numbered from 1 to n,
% and a set of connections, or edges, between them.
% In order to plot G, two matrices are needed.
% The adjacency matrix, A, has a(i,j) nonzero if and
% only if node i is connected to node j. The coordinates
% array, xy, is an n-by-2 or n-by-3 matrix with the position
% for node i in the i-th row, xy(i,:) = [x(i) y(i)],
% or xy(i,:) = [x(i) y(i) z(i)].
%
% gplotg(A,xy,lc) uses line type and color instead of the
% default, 'r-'. For example, lc = 'g:'. See PLOT.
if nargin < 3
lc = 'r-';
end
[i, j] = find(A);
[~, p] = sort(max(i,j));
i = i(p);
j = j(p);
% Create a long, NaN-seperated list of line segments,
% rather than individual segments.
X = [ xy(i,1) xy(j,1) NaN*ones(size(i))]';
Y = [ xy(i,2) xy(j,2) NaN*ones(size(i))]';
X = X(:);
Y = Y(:);
if size(xy,2) == 2
h = plot (X, Y, lc);
else
set(gca,'drawmode','fast');
Z = [ xy(i,3) xy(j,3) NaN*ones(size(i))]';
Z = Z(:);
h = plot3 (X, Y, Z, lc);
end
axis equal;
axis off;
if nargout >= 1
handle = h;
end

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function handle = gplotmap(A,xy,map,pcolor,ecolor)
% GPLOTMAP : Plot a partitioned graph in 2 or 3 dimensions.
%
% gplotmap(A,xy,map) plots the n-vertex graph specified
% by the n by n adjacency (or Laplacian) matrix A
% and the n by 2 or 3 matrix of coordinates xy.
% Argument map is an n-vector of part numbers, which
% assigns each vertex to a part.
%
% By default, edges that join different parts are omitted, and
% the picture shows each part in a different color. The call
%
% gplotmap(A,xy,map,pcolor,ecolor)
%
% uses color "pcolor" for the parts and "ecolor" for the
% edges between the parts. If "pcolor" has multiple rows,
% each part gets the color of one row (in cyclic order).
[n,n] = size(A);
if nargin < 3,
map = zeros(1,n);
end;
if nargin < 4
pcolor = [];
end;
if nargin < 5,
ecolor = [];
end;
parts = setfilter(map);
nparts = length(parts);
if length(pcolor) == 0,
pcolor = hsv(nparts);
pcolor = pcolor(randperm(nparts),:);
end;
clf reset
colordef(gcf,'black')
if size(xy,2) == 2
axis([min(xy(:,1)) max(xy(:,1)) min(xy(:,2)) max(xy(:,2))]);
else
axis([min(xy(:,1)) max(xy(:,1)) min(xy(:,2)) max(xy(:,2)) ...
min(xy(:,3)) max(xy(:,3))]);
end;
axis equal;
axis off;
hold on
% Count and plot the separating edges.
[i,j] = find(A);
f = find(map(i) > map(j));
%comm volume
volume = 0;
for r=1:size(A,1)
idx = find(i == r);
volume = volume + length(unique(map(j(idx)))) - 1;
end
if length(f)
xlabel( [int2str(length(f)) ' cut edges on ' int2str(nparts) ' partitions, '...
'communication volume ' int2str(volume) ],'visible','on');
if length(ecolor)
cut = sparse(i(f),j(f),1,n,n);
set(gplotg(cut,xy,'-'),'color',ecolor);
end;
else
xlabel('0 cut edges','visible','on');
end;
% Plot each piece.
ncolor = 1;
for partnumber = parts;
c = pcolor(ncolor,:);
ncolor = rem(ncolor, size(pcolor,1)) + 1;
part = find(map == partnumber);
set(gplotg(A(part,part),xy(part,:),'-'),'color',c);
if n < 500,
if size(xy,2) == 2
set(plot(xy(part,1),xy(part,2),'o'),'color',c);
else
set(plot3(xy(part,1),xy(part,2),xy(part,3),'o'),'color',c);
end;
end;
end;
hold off

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function handle = gplotpart(A,xy,part1,color1,color2,color3)
% GPLOTPART : Plot a partitioned graph in 2 or 3 dimensions.
%
% gplotpart(A,xy,part1) plots the n-vertex graph specified
% by the n by n adjacency (or Laplacian) matrix A
% and the n by 2 or 3 matrix of coordinates xy.
% Argument part1 is a vector of vertex names (integers 1:n);
% the subgraphs induced by part1 and its complement are plotted
% in different colors, with the edges joining them in a third color.
% Three more optional arguments give the three colors.
if nargin < 3
part1 = [];
end
if nargin < 4
color1 = 'yellow';
end
if nargin < 5
color2 = 'cyan';
end
if nargin < 6
color3 = 'red';
end
[n,~] = size(A);
part1 = part1(:);
part2 = 1:n;
part2(part1)=zeros(size(part1));
part2 = find(part2);
part2 = part2(:);
cut = spaugment(A(part1,part2),1);
cutxy = xy([part1; part2],:);
clf reset
%colordef(gcf,'black')
if size(xy,2) == 2
axis([min(xy(:,1)) max(xy(:,1)) min(xy(:,2)) max(xy(:,2))]);
else
axis([min(xy(:,1)) max(xy(:,1)) min(xy(:,2)) max(xy(:,2)) ...
min(xy(:,3)) max(xy(:,3))]);
end
axis equal;
axis off;
hold on
if ~isempty(part1) && ~isempty(part2)
set(gplotg(cut,cutxy,'-'),'color',color3);
xlabel([int2str(cutsize(A,part1)) ' cut edges'],'visible','on');
else
xlabel('0 cut edges','visible','on');
end
if ~isempty(part1)
set(gplotg(A(part1,part1),xy(part1,:),'-'),'color',color1);
if n < 500
if size(xy,2) == 2
set(plot(xy(part1,1),xy(part1,2),'o'),'color',color1);
else
set(plot3(xy(part1,1),xy(part1,2),xy(part1,3),'o'),'color',color1);
end
end
end
if ~isempty(part2)
set(gplotg(A(part2,part2),xy(part2,:),'-'),'color',color2);
if n < 500
if size(xy,2) == 2
set(plot(xy(part2,1),xy(part2,2),'o'),'color',color2);
else
set(plot3(xy(part2,1),xy(part2,2),xy(part2,3),'o'),'color',color2);
end
end
end
hold off

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function highlight(A,xy,sep,highcolor,meshcolor,dotsize)
% HIGHLIGHT : Plot a mesh with subgraph highlighted.
%
% highlight(A,xy,sep) plots a picture of the mesh A with coordinates xy,
% highlighting the subgraph induced by the vertices in sep.
% Optional fourth and fifth arguments are color/linetype of highlight and mesh.
% Optional sixth argument is size of highlighting dot.
if nargin < 4
highcolor = 'w-';
end
if nargin < 5
meshcolor = 'r-';
end
if nargin < 6
dotsize = 15;
end
[n,n] = size(A);
[i,j] = find(A);
% Plot main graph with vertices before edges.
[ignore, p] = sort(max(i,j));
i = i(p);
j = j(p);
% Create a long, NaN-seperated list of line segments,
% rather than individual segments.
X = [ xy(i,1) xy(j,1) NaN*ones(size(i))]';
Y = [ xy(i,2) xy(j,2) NaN*ones(size(i))]';
X = X(:);
Y = Y(:);
xymin = min(xy);
xymax = max(xy);
plot (X, Y, meshcolor,'erasemode','none');
axis([xymin(1) xymax(1) xymin(2) xymax(2)]);
axis('equal');
axis('off');
hold on;
% Highlight sep set.
B = A(sep,sep);
xB = xy(sep,1);
yB = xy(sep,2);
[i,j] = find(B);
X = [xB(i) xB(j)]';
Y = [yB(i) yB(j)]';
plot (X, Y, highcolor,'erasemode','none');
if n < 1200
handle = plot(xB,yB,[highcolor(1) '.'],'erasemode','none');
set(handle,'markersize',dotsize)
end
hold off;

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function spypart(S, rp, cp);
%SPYPART Spy plot with partitioning.
% SPYPART(S,rp,cp) plots the sparsity pattern of a matrix S,
% with lines marking a block partition described by
% rp (rows) and cp (columns).
% If S is square, cp may be omitted and defaults to rp.
%
% Partitions are specified as in the output of DMPERM:
% There are length(rp)-1 row blocks, of sizes diff(rp), with rp(1)=1.
if (nargin < 3), cp = rp; end
clf
colordef(gcf,'black')
[m,n] = size(S);
if max(m,n) > 100
spy(S,3)
else
spy(S,5)
end
hold on
if length(rp) > 2
k = length(rp)-2;
X = [zeros(1,k); n+ones(1,k)];
Y = rp(2:k+1) - 0.5;
Y = [Y; Y];
plot(X,Y,'w-')
end
if length(cp) > 2
k = length(cp)-2;
X = cp(2:k+1) - .5;
X = [X; X];
Y = [zeros(1,k); m+ones(1,k)];
plot(X,Y,'w-')
end
axis('ij')
hold off

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% add necessary paths for the Partitioning Toolbox
addpath ../datasets/
addpath ../datasets/Mesh_generation/
addpath ../datasets/2d_meshes
addpath ../datasets/Roads/
addpath ../external/
addpath Visualization/

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function [Inc] = adjacency_to_incidence(Adj)
A = Adj;
for i = 1:size(A,1)
A(i,i) = 0;
end
Inc = adj_2_inc(A);
% Inc = adj2inc(A);
Inc = Inc';
end
function Ic = adj_2_inc(A)
% adjacency2incidence - convert an adjacency matrix to an incidence matrix
%
% Ic = adjacency2incidence(A);
%
% A(i,j) = 1 iff (i,j) is an edge of the graph.
% For each edge number k of the graph linking (i,j)
% Ic(i,k)=1 and Ic(j,k)=-1
%
% Ic is a sparse matrix.
% Ic is also known as the graph gradient.
%
% Copyright (c) 2006 Gabriel Peyre
%% compute list of edges
[i,j,s] = find(sparse(A));
I = find(i<=j);
i = i(I);
j = j(I);
% number of edges
n = length(i);
% number of vertices
nverts = size(A,1);
%% build sparse matrix
s = [ones(n,1); -ones(n,1)];
is = [(1:n)'; (1:n)'];
js = [i(:); j(:)];
Ic = sparse(is,js,s,n,nverts);
Ic = Ic';
% fix self-linking problem (0)
a = find(i==j);
if not(isempty(a))
for t=a'
Ic(i(t),t) = 1;
end
end
end

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function [part1,part2] = bisection_coordinate(A,xy,picture)
% COORDPART : Coordinate bisection partition of a mesh.
%
% [part1,part2] = bisection_coordinate(A,xy,picture) returns a list of the vertices on one side of a partition
% obtained by bisection perpendicular to a coordinate axis. We try every
% coordinate axis and return the best cut.
% Input A is the adjacency matrix of the mesh;
% each row of xy is the coordinates of a point in d-space.
%
% coordpart(A,xy,1) also draws a picture.
d = size(xy,2);
best_cut = inf;
for dim = 1:d
v = zeros(d,1);
v(dim) = 1;
[p1,p2] = partition(xy,v);
this_cut = cutsize(A,p1);
if this_cut < best_cut
best_cut = this_cut;
part1 = p1;
part2 = p2;
end
end
if picture
clf reset
gplotpart(A,xy,part1);
title('Coordinate bisection')
end
end

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function [part1,part2] = bisection_inertial(A,xy,picture)
% INERTPART : Inertial partition of a graph.
%
% p = inertpart(A,xy) returns a list of the vertices on one side of a partition
% obtained by bisection with a line or plane normal to a moment of inertia
% of the vertices, considered as points in Euclidean space.
% Input A is the adjacency matrix of the mesh (used only for the picture!);
% each row of xy is the coordinates of a point in d-space.
%
% inertpart(A,xy,1) also draws a picture.
%
% See also PARTITION
%disp(' ');
%disp(' Numerical Computing @ USI Lugano: ');
%disp(' Implement inertial bisection');
%disp(' ');
% Steps
% 1. Calculate the center of mass.
m = sum(xy, 1) / size(xy, 1);
xm = m(1);
ym = m(2);
% 2. Construct the matrix M.
% (Consult the pdf of the assignment for the creation of M)
h = [(xy(:, 1) - xm) (xy(:, 2) - ym)];
X1 = sum(h(:, 1) .^ 2);
X2 = sum(h(:, 1) .* h(:, 2));
X3 = sum(h(:, 2) .^ 2);
M = [X1 X2; X2 X3];
% 3. Calculate the smallest eigenvector of M.
[u, ~] = eigs(M,1,'smallestabs');
a = u(1);
b = u(2);
% 4. Find the line L on which the center of mass lies.
S = -b * (xy(:, 1) - xm) + a * (xy(:, 2) - ym);
Sm = median(S);
% 5. Partition the points around the line L.
% (you may use the function partition.m)
part1 = find(S < Sm);
part2 = find(S >= Sm);
if picture == 1
gplotpart(A,xy,part1);
title('Inertial bisection using the Fiedler Eigenvector');
end
end

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function [part1,part2] = bisection_metis(A,xy,picture)
% METISPART : Partition a graph using Metis default method.
%
% p = metispart(A) returns a list of the vertices on one side of
% a partition obtained by Metis 4.0 applied to the graph of A.
%
% Optional arguments:
% metispart(A,xy) draws a picture of the partitioned graph,
% using the rows of xy as vertex coordinates.
% [p1,p2] = metispart(...) also returns the list of vertices
% on the other side of the partition.
%
% See also METISMEX (which accepts all the Metis options),
map = metismex('PartGraphRecursive',A,2);
[part1,part2] = other(map);
if picture
gplotpart(A,xy,part1);
title('Metis bisection')
end
end

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function [part1,part2] = bisection_spectral(A,xy,picture)
% bisection_spectral : Spectral partition of a graph.
%
% [part1,part2] = bisection_spectral(A) returns a partition of the n vertices
% of A into two lists part1 and part2 according to the
% spectral bisection algorithm of Simon et al:
% Label the vertices with the components of the Fiedler vector
% (the second eigenvector of the Laplacian matrix) and partition
% them around the median value or 0.
%disp(' ');
%disp(' Numerical Computing @ USI Lugano: ');
%disp(' Implement inertial bisection');
%disp(' ');
% Steps
% 1. Construct the Laplacian.
[i, j, ~] = find(triu(A));
G = graph(i, j);
L = laplacian(G);
% 2. Calculate its eigensdecomposition.
[V, ~] = eigs(L,2,'smallestabs');
W = V(:,2);
% 3. Label the vertices with the components of the Fiedler vector.
[values, indexes] = sort(W);
% 4. Partition them around their median value, or 0.
cut = size(values(values < 0), 1);
part1 = indexes(1:cut);
part2 = indexes(cut+1:end);
if picture == 1
gplotpart(A,xy,part1);
title('Spectral bisection using the Fiedler Eigenvector');
elseif picture == 2
drawgraph(A, xy, W, part1, part2)
% 3d plot
title('Spectral bisection using the Fiedler Eigenvector');
end
end
function drawgraph(A, xy, W, part1, part2)
scatter3(xy(:, 1), xy(:, 2), W, 30, W, 'filled')
colorbar
hold on
gplot(A(part1, part1), xy(part1, :), '-k');
set(gca,'ColorOrder', ones(size(part2, 2), 3) .* 0.3)
gplot(A(part2, part2), xy(part2, :));
P1P2 = zeros(size(A));
P1P2(part1, part2) = A(part1, part2);
P1P2(part2, part1) = A(part2, part1);
gplot(P1P2, xy, '-r');
hold off
end

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function A = blockdiags(B,d,m,n)
% BLOCKDIAGS : Create sparse block diagonal matrices.
%
% A = blockdiags(B,d,m,n).
%
% Blockdiags, which generalizes the function "spdiags",
% produces a sparse matrix with specified block diagonals.
%
% A is an m*k-by-n*k matrix, or an m-by-n matrix of k-by-k blocks.
% The nonzero blocks of A are located on p block diagonals.
% B is a min(m,n)*k-by-p*k matrix whose k-by-k block columns
% are the block diagonals of A.
% (Alternatively, B is k-by-p*k, and then A is block Toeplitz.)
% d is a vector of p integers in the range -m+1 : n-1,
% specifying which block diagonals in A are to be nonzero.
% The values of p and k are determined from the dimensions of B and d.
%
% For k=1 this is exactly the same as A = spdiags(B,d,m,n); see spdiags
% for examples of use. For k>1 this is conceptually the same as spdiags,
% but k-by-k blocks replace matrix elements everywhere.
%
% For example, the following code sets A to the n^2-by-n^2 matrix of
% the Laplacian on an n-by-n square grid; the matrix is block tridiagonal,
% and the nonzero blocks themselves are tridiagonal or the identity.
%
% a = blockdiags ([-1 4 -1], -1:1, n, n);
% I = speye (n, n);
% A = blockdiags ([-I a -I], -1:1, n, n);
if nargin ~= 4
error ('Usage: A = blockdiags(B,d,m,n)');
end
k = length(d);
[nrB,ncB] = size(B);
p = ncB/k;
% Check for reasonable input.
if any(size(m)>1) | any(size(n)>1)
error ('blockdiags(B,d,m,n): m or n not scalar');
end
if min(size(d)~=1)
error ('blockdiags(B,d,m,n): d not a vector');
end
if any(rem(size(B),p))
error ('blockdiags(B,d,m,n): block size does not divide size of B');
end
% Process A in compact form.
[i,j,a] = find(B);
nzB = length(a);
% Duplicate rows of B if A is to be block Toeplitz.
if nrB == p
nzA = m*nzB;
range = [0:nzA-1]';
r = 1 + rem(range,nzB);
a = a(r);
i = i(r);
j = j(r);
a = a(:);
i = i(:) + p * floor(range/nzB);
j = j(:);
end
% Rows of B are rows of A. Shift columns appropriately.
dd = d(1+floor((j-1)/p));
dd = dd(:);
j = 1 + rem(j-1,p) + p * (dd + floor((i-1)/p));
% Clip columns that shifted off the matrix.
f = find(j>=1 & j<=n*p);
a = a(f);
i = i(f);
j = j(f);
% Put A back into sparse form.
A = sparse (i,j,a,m*p,n*p);

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function [ne,edges] = cutsize(A,map);
% CUTSIZE : Edges cut by a vertex partition.
%
% ne = cutsize(A,part) : part is a list of vertices of A;
% ne is the number of edges crossing the cut.
% [ne,edges] = cutsize(A,part): Same, edges is the crossing edges as rows [i j].
%
% "part" may also be a map, with one entry per vertex.
% Take input in either order unless A is 1 by 1.
if size(map,1) == size(map,2) & size(map,1) > 1
t = A;
A = map;
map = t;
end;
% Convert input to a map in any case.
if length(map) ~= length(A) | max(map) == length(A)
[ignore,map] = other(map,A);
end;
[i,j] = find(A);
f = find(map(i) > map(j));
ne = length(f);
edges = [i(f) j(f)];

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load Toy_meshes;
addpaths_GP;
[W1,c1] = grid5rec(10,100);
[W2,c2] = grid5rec(100,10);
[W3,c3] = grid5recRotate(100,10,-45);
[W4,c4] = gridt(40);
[W5,c5] = grid9(30);
W = {W1, W2, W3, W4, W5, Smallmesh, Tapir, Eppstein};
c = {c1, c2, c3, c4, c5, Smallmesh_coords, Tapir_coords, Eppstein_coords};
fprintf('implementation\tgrid5rec(10,100)\tgrid5rec(100,10)\tgrid5recRotate(100,10,-45)\tgridt(40)\tgrid9(30)\tSmallmesh\tTapir\tEppstein\n');
fprintf('coordinate\t');
runtest(@(a, b) bisection_coordinate(a, b, 0));
fprintf('metis \t');
runtest(@(a, b) bisection_metis(a, b, 0));
fprintf('spectral\t');
runtest(@(a, b) bisection_spectral(a, b, 0));
fprintf('inertial\t');
runtest(@(a, b) bisection_inertial(a, b, 0));
function runtest(implementation)
global W
global c
for i=1:8
out = implementation(W{i}, c{i});
ne = cutsize(W{i},out);
fprintf('%d\t', ne);
end
fprintf('\n');
end

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load Toy_meshes;
addpaths_GP;
[A, xy] = grid9(50);
bisection_spectral(A, xy, 2);
%matlab2tikz('../../ex3_grid9.tex');
figure;
bisection_spectral(Smallmesh, Smallmesh_coords, 2);
%matlab2tikz('../../ex3_small.tex');
figure;
bisection_spectral(Eppstein, Eppstein_coords, 2);
%matlab2tikz('../../ex3_eppstein.tex');

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function [out1,out2] = other(in1,in2);
% OTHER : Find the other part of a partition, or
% convert a partition to the other representation.
%
% part2 = other(part1,n) : part1 is a list of subscripts in 1:n;
% part2 becomes the complementary list.
% part2 = other(part1,A) : Same, except n is taken as max(size(A)).
% [part2,p] = other(part1,n) : Also returns 0/1 partition vector of length n.
% [part2,p] = other(part1,A) : Same.
% [part1,part2] = other(p) : Converts 0/1 vector to lists of subscripts.
if nargin == 1
out1 = find(in1 == 0);
out2 = find(in1 ~= 0);
else
if max(size(in2)) > 1
in2 = max(size(in2));
end;
out2 = full(spones(sparse(1,in1,1,1,in2)));
out1 = find(out2 == 0);
end;

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function [a,b] = partition(xyz,v)
% PARTITION : Partition points by a plane.
%
% [a,b] = partition(xyz,v):
% Each row of xyz is an input point in d-space.
% Input v is a vector, giving a direction normal to the partitioning plane.
%
% The output is two vectors of integers,
% the indices of the points on each side of the plane.
% Points on the plane are assigned to balance the cut.
[n,d] = size(xyz);
v = v(:); % Make v a column vector
if length(v) ~= d
error('v must be a d-vector')
end;
dotprod = xyz * v;
split = median(dotprod);
a = find(dotprod < split);
b = find(dotprod > split);
c = find(dotprod == split);
nc = length(c);
if nc
na = length(a);
nca = max([ceil(n/2)-na, 0]);
nca = min(nca,nc);
if nca > 0
a = [a; c(1:nca)];
end;
if nca < nc
b = [b; c(nca+1:nc)];
end;
end;
a = a';
b = b';

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function [map,sepij,sepA] = rec_bisection(method,levels,A,varargin)
% DICE Separate a graph recursively.
%
% [map,sepij,sepA] = DICE(method,levels,A,arg...) partitions the
% mesh or graph A recursively by a specified method. 'method' is the name
% of the 2-way edge separator function to call. levels is the number of
% levels of partitioning. A is the adjacency matrix of the graph. arg2,
% arg3, arg4 are optional additional arguments to the 2-way function. arg2
% is special: If it has the same number of rows as A, then the recursive
% calls use the appropriate subset of arg2 as well as of A. This is useful
% if the partitioner uses xy coords. map is a vector of integers from 0 to
% 2^levels-1, indexed by vertex, giving the partition number for each
% vertex. sepij is a list of separating edges; each row is an edge [i j].
% sepA is the adjacency matrix of the graph less the separating edges.
%
% For example, dice('geopart',7,A,xy,0,ntries) splits A into 2^7=128
% parts, using a call at each stage that looks like
% geopart(A,xy,0,ntries).
minpoints = 8; % Don't separate pieces smaller than this.
nargcall = nargin - 2;
n = size(A,1);
if n < minpoints || levels < 1
map = zeros(1,n);
else
% Call the partitioner to split the graph, giving one part.
[p1,p2] = feval(method, A, varargin{:});
% Call recursively.
vararginp1 = varargin;
vararginp2 = varargin;
if nargcall >= 2
if size(varargin{1},1) == n
vararginp1{1} = varargin{1}(p1,:);
vararginp2{1} = varargin{1}(p2,:);
end
end
mapa = rec_bisection(method,levels-1,A(p1,p1),vararginp1{:});
mapb = rec_bisection(method,levels-1,A(p2,p2),vararginp2{:});
% Set up the whole map.
map = zeros(1,n);
mapb = mapb + max(mapa) + 1;
map(p1) = mapa;
map(p2) = mapb;
end
% Set up the separating edge list and separated graph.
if nargout >= 2
[i,j] = find(A);
f = find(map(i) > map(j));
sepij = [i(f) j(f)];
f = find(map(i) == map(j));
sepA = sparse(i(f), j(f), 1, n, n);
end
end

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function B = setfilter(A)
% SETFILTER : Sort and remove duplicates.
%
% B = setfilter(A) returns a row vector with one occurrence
% of each different element of A, in sorted order.
if length(A) == 0
B = [];
return;
end;
B = sort(A(:));
B(find(diff(B)==0)) = [];
B = B.';

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\usepackage{ifthen}
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\begin{table}[h]
\begin{center}
\scalebox{0.8} {%
\begin{tabular}{|p{0.02cm}p{16cm}|}
\hline
&\\
\multicolumn{2}{|c|}{\Large\textbf{Numerical Computing 2020 --- Submission Instructions}}\\
\multicolumn{2}{|c|}{\large\textbf{(Please, notice that following instructions are mandatory: }}\\
\multicolumn{2}{|c|}{\large\textbf{submissions that don't comply with, won't be considered)}}\\
&\\
\textbullet & Assignments must be submitted to \href{https://www.icorsi.ch/course/view.php?id=10018}{iCorsi} (i.e. in electronic format).\\
\textbullet & Provide both executable package and sources (e.g. C/C++ files, Matlab).
If you are using libraries, please add them in the file. Sources must be organized in directories called:\\
\multicolumn{2}{|c|}{\textit{Project\_number\_lastname\_firstname}}\\
& and the file must be called:\\
\multicolumn{2}{|c|}{\textit{project\_number\_lastname\_firstname.zip}}\\
\multicolumn{2}{|c|}{\textit{project\_number\_lastname\_firstname.pdf}}\\
\textbullet & The TAs will grade your project by reviewing your project write-up, and looking at the implementation
you attempted, and benchmarking your code's performance.\\
\textbullet & You are allowed to discuss all questions with anyone you like; however: (i) your submission must list anyone you discussed problems with and (ii) you must write up your submission independently.\\
\hline
\end{tabular}
}
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\documentclass[unicode,11pt,a4paper,oneside,numbers=endperiod,openany]{scrartcl}
\usepackage{graphicx}
\usepackage{subcaption}
\usepackage{amsmath}
\input{assignment.sty}
\usepackage{pgfplots}
\pgfplotsset{compat=newest}
\usetikzlibrary{plotmarks}
\usetikzlibrary{arrows.meta}
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\usepackage{amsmath}
\usepackage{subcaption}
\hyphenation{PageRank}
\hyphenation{PageRanks}
\begin{document}
\setassignment
\setduedate{Wednesday, 4 November 2020, 11:55 PM}
\serieheader{Numerical Computing}{2020}{Student: Claudio Maggioni}{Discussed with: --}{Solution for Project 3}{}
\newline
\assignmentpolicy
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Install METIS 5.0.2, and the corresponding Matlab mex interface}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Implement various graph partitioning algorithms \punkte{60}}
I summarize the various benchmark results in table \ref{table:bisection}. Please note that this table
can be generated at will with the script \texttt{ex2\_bisection\_table.m}.
\section{Visualize the Fiedler eigenvector\punkte{10}}
In figure \ref{fig:run1} there are graph outputs respectively from \textit{Grid9}, \textit{Small}, and \textit{Eppstein}.
\begin{figure}[h]
\begin{subfigure}{0.5\textwidth}
\centering
\includegraphics[trim=50 200 50 200,clip,width=\textwidth]{ex3_grid9.pdf}
\caption{Plot for \textit{Grid9}}
\end{subfigure}
\begin{subfigure}{0.5\textwidth}
\centering
\includegraphics[trim=50 200 50 200,clip,width=\textwidth]{ex3_small.pdf}
\caption{Plot for \textit{Small}}
\end{subfigure}
\begin{subfigure}{0.5\textwidth}
\includegraphics[trim=50 200 50 200,clip,width=\textwidth]{ex3_eppstein.pdf}
\caption{Plot for \textit{Eppstein}}
\end{subfigure}
\caption{Graph outputs for the 3 adjacency matrices.}
\label{fig:run1}
\end{figure}
\section{Recursively bisecting meshes \punkte{20}}
Summarize your results in table \ref{table:Rec_bisection}.
\section{Compare recursive bisection to direct $k$-way partitioning\punkte{10}}
Summarize your results in table \ref{table:Compare_Metis}.
\begin{table}[h]
\caption{Bisection results}
\centering
\begin{tabular}{|l|r|r|r|r|} \hline\hline
Mesh & Coordinate & Metis 5.0.2 & Spectral & Inertial \\ \hline
grid5rect(10,100)& 10 & 10 & 10 & 10 \\
grid5rect(100,10)& 10 & 10 & 10 & 10 \\
grid5recRotate(100,10,-45)& 18 & 10 & 10 & 10 \\
gridt(40) & 58 & 58 & 58 & 58 \\
grid9(30) & 88 & 92 & 104 & 88 \\
Smallmesh & 25 & 13 & 12 & 30 \\
Tapir & 55 & 34 & 18 & 49 \\
Eppstein & 42 & 48 & 45 & 45 \\ \hline \hline
\end{tabular}
\label{table:bisection}
\end{table}
\begin{table}[h]
\caption{Edge-cut results for recursive bi-partitioning.}
\centering
\begin{tabular}{|l|r|r|r|r|r|} \hline\hline
Case & Spectral & Metis 5.0.2 & Coordinate & Inertial \\ \hline
mesh3e1 & & & & \\
airfoil1 & & & & \\
3elt & & & & \\
barth4 & & & & \\
crack & & & & \\ \hline \hline
\end{tabular}
\label{table:Rec_bisection}
\end{table}
\begin{table}[h]
\caption{Comparing the number of cut edges for recursive bisection and direct multiway partitioning in Metis 5.0.2.}
\centering
\begin{tabular}{|l|r|r|r|r|} \hline\hline
Partitions & crack & airfoil1 \\ \hline
16 & & \\
32 & & \\ \hline \hline
\end{tabular}
\label{table:Compare_Metis}
\end{table}
\end{document}

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