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Class jnt.linear_algebra.SparseVectorDouble

java.lang.Object
   |
   +----jnt.linear_algebra.SparseVectorDouble

public class SparseVectorDouble

extends Object

The nonzero value, together with its offset are stored. Thus, the 5-element vector [0.0, 2.1, 0.0, 7.0, 3.5] (with three nonzeros) could be represented as a sparse vector by the following contstructor:

double val[] = new double[3];
int    ind[] = new int[3];
val[0] = 2.1;  ind[0] = 1;
val[1] = 7.0;  ind[1] = 3;
val[2] = 3.5;  ind[2] = 4;
SparseVectorDouble S = new SparseVectorDouble(5, 3, val, ind);

SparseVectorDouble S = new SparseVectorDouble(5, 3);
S.set(1, 2.1);
S.set(3, 7.0);
S.set(4, 3.5);

The elements are unoreded, and offsets begin at 0. Note, also, that sparse vector representations are not unique -- the elements are unordered, and explicit zeros could be stored.

Version:

0.5

Author:

Roldan Pozo

SparseVectorDouble(int)

Initialize a sparse vector of a given dimension.

SparseVectorDouble(int, int)

Initialize a sparse vector of a given dimension and nonzeros.

SparseVectorDouble(int, int, double[], int[])

Initialize a sparse vector of dimension N, with nz nonzeros, given values from val[] and integer positions in ind[].

add(SparseVectorDouble)

add_eq(double[], SparseVectorDouble)

copy()

Create a copy of an existing sparse vector.

find(int)

Find out if a sparse vector contains an entry at a a given position in it's dense representation.

gather(double[])

gather(double[], int[], int[])

Pick off certain elements from a long dense vector and put them into nonzero values (val).

get(int)

get_index(int)

get_val(int)

num_nonzeros()

Determine the number of entries (nonzeros) in sparse vector.

print()

scatter(double[], SparseVectorDouble)

set(int, double)

set_index(int, int)

set_value(int, double)

set_value_index(int, double, int)

size()

Return the dimension of the sparse vector.

 public SparseVectorDouble(int N)

Initialize a sparse vector of a given dimension. No storage space for nonzeros is actually created.

Parameters:

N - the dimension of the vector (not the number of nonzeros.)

 public SparseVectorDouble(int N,
                           int nz)

Initialize a sparse vector of a given dimension and nonzeros.

Parameters:

N - the dimension of the vector.

nz - the number of nonzeros.

 public SparseVectorDouble(int N,
                           int nz,
                           double val[],
                           int index[])

Initialize a sparse vector of dimension N, with nz nonzeros, given values from val[] and integer positions in ind[].

Parameters:

N - the dimension of the vector.

nz - the number of nonzeros.

val[nz] - vector of nonzero values

ind[nz] - vector of positions of elements in val[].

 public SparseVectorDouble copy()

Create a copy of an existing sparse vector.

 public int find(int position)

Find out if a sparse vector contains an entry at a a given position in it's dense representation.

Parameters:

position - (in): offset of dense vector to find.

Returns:

offset in compressed format, if found; -1, otherwise.

 public final double get(int position)

 public final double get_val(int position)

 public final void set_value(int position,
                             double val)

 public final int get_index(int position)

 public final void set_index(int position,
                             int index)

 public final void set_value_index(int position,
                                   double val,
                                   int ind)

 public final void set(int position,
                       double val)

 public static void add_eq(double x[],
                           SparseVectorDouble S)

 public void scatter(double x[],
                     SparseVectorDouble S)

 public SparseVectorDouble add(SparseVectorDouble S)

 public void print()

 public static void gather(double val[],
                           int ind[],
                           int x[])

Pick off certain elements from a long dense vector and put them into nonzero values (val). The positions to pick out are listed in ind. For example, if ind = (1, 2, 4) and x= ( 3.1, 4.2, 5.3, 6.4, 7.5) then val wolud be set to ( 4.2, 5.3, 7.5 )

Parameters:

val - (out) vector of nonzeros

ind - (in) vector of indicies

x - (in) the dense vector to extract from

 public SparseVectorDouble gather(double x[])

 public int size()

Return the dimension of the sparse vector.

 public int num_nonzeros()

Determine the number of entries (nonzeros) in sparse vector.

Returns:

the number of explicitly-stored elemented in the sparse vector.

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