In Datastructures and algorithms in Java, Part 2 I introduced a variety of techniques for searching and sorting one-dimensional arrays, which are the simplest arrays. In this article we'll explore multidimensional arrays. I'll introduce the three techniques for creating multidimensional arrays, then show you how to use the Matrix Multiplication algorithm to multiply elements in a two-dimensional array. I'll also introduce ragged arrays and show you why they are popular for big data applications. Finally, I will answer the question of whether an array is or is not a Java object.

## Introducing multidimensional arrays

A *multidimensional array* associates each element in the array with multiple indexes. The most commonly used multidimensional array is the *two-dimensional array*, also known as a *table* or *matrix*. A two-dimensional array associates each of its elements with two indexes.

We can conceptualize a two-dimensional array as a rectangular grid of elements divided into rows and columns. We use the `(row, column)`

notation to identify an element, as shown in Figure 1.

Because two-dimensional arrays are so commonly used, I'll focus on them. What you learn about two-dimensional arrays can be generalized to higher-dimensional ones.

## Creating two-dimensional arrays

There are three techniques for creating a two-dimensional array in Java:

- Using an initializer
- Using the keyword
`new`

- Using the keyword
`new`

with an initializer

### Using an initializer to create a two-dimensional array

The initializer-only approach to creating a two-dimensional array has the following syntax:

`'{' [`*rowInitializer* (',' *rowInitializer*)*] '}'

* rowInitializer* has the following syntax:

`'{' [`*expr* (',' *expr*)*] '}'

This syntax states that a two-dimensional array is an optional, comma-separated list of row initializers appearing between open- and close-brace characters. Furthermore, each row initializer is an optional, comma-separated list of expressions appearing between open- and close-brace characters. Like one-dimensional arrays, all expressions must evaluate to compatible types.

Here's an example of a two-dimensional array:

`{ { 20.5, 30.6, 28.3 }, { -38.7, -18.3, -16.2 } }`

This example creates a table with two rows and three columns. Figure 2 presents a conceptual view of this table along with a memory view that shows how Java lays out this (and every) table in memory.

Figure 2 reveals that Java represents a two-dimensional array as a one-dimensional row array whose elements reference one-dimensional column arrays. The row index identifies the column array; the column index identifies the data item.

### Keyword new-only creation

The keyword `new`

allocates memory for a two-dimensional array and returns its reference. This approach has the following syntax:

`'new' `*type* '[' *int_expr1* ']' '['*int_expr2* ']'

This syntax states that a two-dimensional array is a region of (positive) * int_expr1* row elements and (positive)

*column elements that all share the same*

`int_expr2`

*. Furthermore, all elements are zeroed. Here's an example:*

`type`

`new double[2][3] // Create a two-row-by-three-column table.`

### Keyword new and initializer creation

The keyword `new`

with an initializer approach has the following syntax:

`'new' `*type* '[' ']' [' ']' '{' [*rowInitializer* (',' *rowInitializer*)*] '}'

where * rowInitializer* has the following syntax:

`'{' [`*expr* (',' *expr*)*] '}'

This syntax blends the previous two examples. Because the number of elements can be determined from the comma-separated lists of expressions, you don't provide an * int_expr* between either pair of square brackets. Here is an example:

`new double [][] { { 20.5, 30.6, 28.3 }, { -38.7, -18.3, -16.2 } }`

## Two-dimensional arrays and array variables

By itself, a newly-created two-dimensional array is useless. Its reference must be assigned to an *array variable* of a compatible type, either directly or via a method call. The following syntaxes show how you would declare this variable:

*type* *var_name* '[' ']' '[' ']'
*type* '[' ']' '[' ']' *var_name*

Each syntax declares an array variable that stores a reference to a two-dimensional array. It's preferred to place the square brackets after * type*. Consider the following examples:

```
double[][] temperatures1 = { { 20.5, 30.6, 28.3 }, { -38.7, -18.3, -16.2 } };
double[][] temperatures2 = new double[2][3];
double[][] temperatures3 = new double[][] { { 20.5, 30.6, 28.3 }, { -38.7, -18.3, -16.2 } };
```

Like one-dimensional array variables, a two-dimensional array variable is associated with a `.length`

property, which returns the length of the row array. For example, `temperatures1.length`

returns 2. Each row element is also an array variable with a `.length`

property, which returns the number of columns for the column array assigned to the row element. For example, `temperatures1[0].length`

returns 3.

Given an array variable, you can access any element in a two-dimensional array by specifying an expression that agrees with the following syntax:

*array_var* '[' *row_index* ']' '[' *col_index* ']'

Both indexes are positive `int`

s that range from 0 to one less than the value returned from the respective `.length`

properties. Consider the next two examples:

```
double temp = temperatures1[0][1]; // Get value.
temperatures1[0][1] = 75.0; // Set value.
```

The first example returns the value in the second column of the first row (`30.6`

). The second example replaces this value with `75.0`

.

If you specify a negative index or an index that is greater than or equal to the value returned by the array variable's `.length`

property, Java creates and throws an `ArrayIndexOutOfBoundsException`

object.

## Multiplying two-dimensional arrays

Multiplying one matrix by another matrix is a common operation in fields ranging from computer graphics, to economics, to the transportation industry. Developers usually use the Matrix Multiplication algorithm for this operation.

How does matrix multiplication work? Let A represent a matrix with *m* rows and *p* columns. Similarly, let B represent a matrix with *p* rows and *n* columns. Multiply A by B to produce a matrix C, with *m* rows and *n* columns. Each *cij* entry in C is obtained by multiplying all entries in A's *ith* row by corresponding entries in B's *jth* column, then adding the results. Figure 3 illustrates these operations.

The following pseudocode expresses Matrix Multiplication in a 2-row-by-2-column and a 2-row-by-1-column table context. (Recall that I introduced pseudocode in Part 1.)

```
// == == == == == ==
// | 10 30 | | 5 | | 10 x 5 + 30 x 7 (260) |
// | | X | | = | |
// | 20 40 | | 7 | | 20 x 5 + 40 * 7 (380) |
// == == == == == ==
DECLARE INTEGER a[][] = [ 10, 30 ] [ 20, 40 ]
DECLARE INTEGER b[][] = [ 5, 7 ]
DECLARE INTEGER m = 2 // Number of rows in left matrix (a)
DECLARE INTEGER p = 2 // Number of columns in left matrix (a)
// Number of rows in right matrix (b)
DECLARE INTEGER n = 1 // Number of columns in right matrix (b)
DECLARE INTEGER c[m][n] // c holds 2 rows by 1 columns
// All elements initialize to 0
FOR i = 0 TO m - 1
FOR j = 0 TO n - 1
FOR k = 0 TO p - 1
c[i][j] = c[i][j] + a[i][k] * b[k][j]
NEXT k
NEXT j
NEXT i
END
```

Because of the three `FOR`

loops, Matrix Multiplication has a time complexity of `O(`

, which is pronounced "Big Oh of *n*^{3})*n* cubed." Matrix Multiplication offers cubic performance, which gets expensive time-wise when large matrixes are multiplied. It offers a space complexity of `O(`

, which is pronounced "Big Oh of *nm*)*n***m*," for storing an additional matrix of *n* rows by *m* columns. This becomes `O(`

for square matrixes.*n*^{2})

I've created a `MatMult`

Java application that lets you experiment with Matrix Multiplication. Listing 1 presents this application's source code.

#### Listing 1. MatMult.java

```
public final class MatMult
{
public static void main(String[] args)
{
int[][] a = {{ 10, 30 }, { 20, 40 }};
int[][] b = {{ 5 }, { 7 }};
dump(a);
System.out.println();
dump(b);
System.out.println();
int[][] c = multiply(a, b);
dump(c);
}
private static void dump(int[][] x)
{
if (x == null)
{
System.err.println("array is null");
return;
}
// Dump the matrix's element values to the standard output in a tabular
// order.
for (int i = 0; i < x.length; i++)
{
for (int j = 0; j < x[0].length; j++)
System.out.print(x[i][j] + " ");
System.out.println();
}
}
private static int[][] multiply(int[][] a, int[][] b)
{
// ====================================================================
// 1. a.length contains a's row count
//
// 2. a[0].length (or any other a[x].length for a valid x) contains a's
// column count
//
// 3. b.length contains b's row count
//
// 4. b[0].length (or any other b[x].length for a valid x) contains b's
// column count
// ====================================================================
// If a's column count != b's row count, bail out
if (a[0].length != b.length)
{
System.err.println("a's column count != b's row count");
return null;
}
// Allocate result matrix with a size equal to a's row count times b's
// column count
int[][] result = new int[a.length][];
for (int i = 0; i < result.length; i++)
result[i] = new int[b[0].length];
// Perform the multiplication and addition
for (int i = 0; i < a.length; i++)
for (int j = 0; j < b[0].length; j++)
for (int k = 0; k < a[0].length; k++) // or k < b.length
result[i][j] += a[i][k] * b[k][j];
// Return the result matrix
return result;
}
}
```

`MatMult`

declares a pair of matrixes and dumps their values to standard output. It then multiplies both matrixes and dumps the result matrix to standard output.

Compile Listing 1 as follows:

`javac MatMult.java`

Run the resulting application as follows:

`java MatMult`

You should observe the following output:

```
10 30
20 40
5
7
260
380
```

## Example of matrix multiplication

Let's explore a problem that is best solved by matrix multiplication. In this scenario, a fruit grower in Florida loads a couple of semitrailers with 1,250 boxes of oranges, 400 boxes of peaches, and 250 boxes of grapefruit. Figure 4 shows a chart of the market price per box for each kind of fruit, in four different cities.

Our problem is to determine where the fruit should be shipped and sold for maximum gross income. To solve that problem, we first reconstruct the chart from Figure 4 as a four-row by three-column price matrix. From this, we can construct a three-row by one-column quantity matrix, which appears below:

```
== ==
| 1250 |
| |
| 400 |
| |
| 250 |
== ==
```

With both matrixes on hand, we simply multiply the price matrix by the quantity matrix to produce a gross income matrix:

```
== == == ==
| 10.00 8.00 12.00 | == == | 18700.00 | New York
| | | 1250 | | |
| 11.00 8.50 11.55 | | | | 20037.50 | Los Angeles
| | X | 400 | = | |
| 8.75 6.90 10.00 | | | | 16197.50 | Miami
| | | 250 | | |
| 10.50 8.25 11.75 | == == | 19362.50 | Chicago
== == == ==
```

Sending both semitrailers to Los Angeles will produce the highest gross income. But when distance and fuel costs are considered, perhaps New York is a better bet for yielding the highest income.

## Ragged arrays

Having learned about two-dimensional arrays, you might now wonder whether it's possible to assign one-dimensional column arrays with different lengths to elements of a row array. The answer is yes. Consider these examples:

```
double[][] temperatures1 = { { 20.5, 30.6, 28.3 }, { -38.7, -18.3 } };
double[][] temperatures2 = new double[2][];
double[][] temperatures3 = new double[][] { { 20.5, 30.6, 28.3 }, { -38.7, -18.3 } };
```

The first and third examples create a two-dimensional array where the first row contains three columns and the second row contains two columns. The second example creates an array with two rows and an unspecified number of columns.

After creating `temperature2`

's row array, its elements must be populated with references to new column arrays. The following example demonstrates, assigning 3 columns to the first row and 2 columns to the second row:

```
temperatures2[0] = new double[3];
temperatures2[1] = new double[2];
```

The resulting two-dimensional array is known as a *ragged array*. Here is a second example:

```
int[][] x = new int[5][];
x[0] = new int[3];
x[1] = new int[2];
x[2] = new int[3];
x[3] = new int[5];
x[4] = new int[1];
```

Figure 5 presents a conceptual view of this second ragged array.

Ragged arrays are useful datastructures because of their memory-saving capability. For example, consider a spreadsheet with the potential for 100,000 rows by 20,000 columns. If we attempt to use a matrix to hold the spreadsheet, we require a great deal of memory. But suppose most of the spreadsheet's cells contain default values, such as 0 for numeric cells and null for nonnumeric cells. If we use a ragged array instead of a matrix, we store only those cells that contain nonnumeric data. (Of course, we need some kind of mapping mechanism that maps spreadsheet (row, column) coordinates to ragged array (row, column) coordinates.)

## Arrays are objects

According to the first sentence of Chapter 10 in the *Java Language Specification* arrays are objects in Java. Under the hood, each array is an instance of a hidden class that inherits `java.lang.Object`

's 11 methods. The array instance overrides `Object`

's `protected Object clone() throws CloneNotSupportedException`

method, allowing the array to be shallowly cloned. The hidden class additionally provides a `.length`

field.

Listing 2's `ArrayIsObject`

source code demonstrates the association between arrays and objects.

#### Listing 2. ArrayIsObject.java

```
public final class ArrayIsObject
{
public static void main(String[] args)
{
double[] a = { 100.5, 200.5, 300.5 };
double[] b = { 100.5, 200.5, 300.5 };
double[] c = b;
System.out.println("a's class is " + a.getClass());
System.out.println("a and b are " + ((a.equals(b)) ? "" : "not ") +
"equal ");
System.out.println("b and c are " + ((b.equals(c)) ? "" : "not ") +
"equal ");
double[] d = (double[]) c.clone();
System.out.println("c and d are " + ((c.equals(d)) ? "" : "not ") +
"equal ");
for (int i = 0; i < d.length; i++)
System.out.println(d[i]);
}
}
```

`ArrayIsObject`

creates `a`

-referenced and `b`

-referenced double precision floating-point arrays with the same contents and lengths. For the `a`

-referenced array, `a.getClass()`

returns `class [D`

, where `[D`

is the name of the array's hidden class.

Despite the two arrays having the same contents, `a.equals(b)`

returns false because `equals()`

compares references (not contents), and `a`

and `b`

contain different references. `b`

's reference is assigned to `c`

, and `b.equals(c)`

returns true because `b`

and `c`

reference the same array. `c.clone()`

creates a shallow clone of `c`

, and a reference to this new array is assigned to `d`

.

To prove that the `d`

-referenced array contains the same contents as the `c`

-referenced array, the `for`

loop iterates over all elements and prints their contents to the standard output. The loop reads the contents of `d`

's read-only `.length`

field to determine over how many elements to iterate.

Compile Listing 2 as follows:

`javac ArrayIsObject.java`

Run the resulting application as follows:

`java ArrayIsObject`

You should observe the following output:

```
a's class is class [D
a and b are not equal
b and c are equal
c and d are not equal
100.5
200.5
300.5
```

## Conclusion to Part 3

This article has introduced multidimensional arrays and the techniques for creating and using them in your Java programs. One very common technique is to multiply one matrix by another matrix. I showed you how to use the Matrix Multiplication algorithm to solve a real-world problem encoded as a pair of two-dimensional arrays. I also introduced ragged arrays and explained why they are especially valuable for big data applications. Finally, I addressed the question of whether arrays are objects in Java. Revealing the hidden class demonstrates that arrays are indeed objects, with all the powers inherited from `java.lang.Object`

.

Stay tuned for Part 4, where we will explore singly linked lists and their algorithms!