MATLAB R2025b Software Installation Guide

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1. Core Concepts and Interface

1. Core Concepts

• MATLAB: The name comes from “Matrix Laboratory”, and its basic data unit is a matrix. Even a single number is treated as a 1×1 matrix. This design makes it extremely efficient for handling arrays and matrix operations.
• Workspace: Stores all variables created in the current session, along with their dimensions and data types. You can view, edit, and save variables here.
• Command Window: Used for interactively entering commands and expressions, displaying results immediately. It is the core area for quick calculations and testing.
• Editor: Used for creating, editing, and debugging script files (.m files) and function files. A script is a series of commands executed in sequence.
• Path: A list of directories that MATLAB uses to find files (data, scripts, functions). You need to add the folder where your files are located to the path to call them directly in the command window.

2. Main Interface Introduction (Default Layout) After starting MATLAB, the main interface typically includes the following windows:

• Current Folder: Displays and browses files in the current working directory.
• Command Window: Used for entering commands.
• Workspace: Displays the size and data types of all created variables.
• Command History: Records previously executed commands.

2. Basic Workflow

Step 1: Basic Operations and Variables

1. Using as a Calculator: Directly enter mathematical expressions in the command window and press <span>Enter</span> to execute.

   3 + 5 * 2
   

   MATLAB will display:

   ans = 13
   

2. Creating Variables: Use the equal sign <span>=</span> for assignment. Variable names are case-sensitive.

   a = 10;
   b = 20;
   c = a + b;
   

   Note: Adding a semicolon <span>;</span> at the end of a command suppresses output, preventing the command window from being flooded.

3. Creating Vectors and Matrices:

• Vector:

  row_vec = [1, 2, 3, 4]   % Row vector (separated by commas or spaces)
  col_vec = [1; 2; 3; 4]   % Column vector (separated by semicolons)
  

• Matrix:

  A = [1, 2, 3; 4, 5, 6; 7, 8, 9] % 3x3 matrix
  

Step 2: Indexing and Matrix Operations

• Indexing: Use parentheses <span>()</span> to access matrix elements. Indexing starts from 1.

  A(2, 3)  % Access the element in the 2nd row, 3rd column of matrix A
  A(1, :)   % Access all columns of the 1st row (colon `:` means "all")
  A(end, 1) % Access the 1st column of the last row
  

• Matrix Operations:

  A * B    % Matrix multiplication
  A .* B   % Element-wise multiplication (dot product, very important)
  A'       % Matrix transpose
  inv(A)   % Matrix inverse (if invertible)
  

Step 3: Scripts and Functions

1. Creating Scripts:

• Click the “New Script” button on the Home tab.
• Enter a series of commands in the editor (e.g., a sequence of commands for plotting complex graphs).
• Save as a <span>.m</span> file (e.g., <span>my_plot.m</span>).
• Click the “Run” button or directly enter the script name (without the extension) in the command window to execute.

% Save as 'calculate_area.m'
function area = calculate_area(radius)
    % Calculate the area of a circle
    area = pi * radius^2;
end

my_area = calculate_area(5)

Step 4: Data Visualization (Plotting)

• 2D Line Plot: Use the <span>plot</span> function.

  x = 0:0.1:2*pi;      % Generate a vector from 0 to 2π with a step of 0.1
  y = sin(x);
  plot(x, y)
  title('Sine Wave')    % Add title
  xlabel('x')          % Add x-axis label
  ylabel('sin(x)')     % Add y-axis label
  grid on              % Show grid
  

• Multiple Figures:

• <span>figure</span> command creates a new figure window.
• <span>subplot(m, n, p)</span> creates a subplot in the current figure window with an m x n grid and activates the p-th subplot.

• Other Common Plots:

• <span>scatter(x, y)</span>: Scatter plot.
• <span>bar(y)</span>: Bar chart.
• <span>histogram(data)</span>: Histogram.

Step 5: Importing and Saving Data

• Importing Data:

• The simplest method: Click “Import Data” on the Home tab, then select a file (e.g., <span>.csv</span>, <span>.xlsx</span>).
• Using functions:

  data_table = readtable('my_data.csv'); % Read as table
  data_matrix = readmatrix('my_data.csv'); % Read as matrix
  load('my_data.mat') % Load .mat file
  

• Saving Workspace:

• Use the <span>save</span> command to save all variables in the workspace to a <span>.mat</span> file.

  save('my_workspace.mat')
  

• Use the <span>load</span> command to reload.

  load('my_workspace.mat')
  

3. Advanced Techniques and Efficiency Improvement

• Using Help: The <span>doc</span> and <span>help</span> commands are your best friends.

  doc plot    % View detailed documentation for the plot function
  help sin    % View brief help for the sin function
  

• Programming Structures:

• Loops:

  for i = 1:10
      disp(i) % Display the value of i in the command line
  end
  

• Conditional Statements:

  if x > 0
      disp('Positive')
  elif x < 0
      disp('Negative')
  else
      disp('Zero')
  end
  

• Logical Indexing: A powerful and efficient method for data selection.

  A = [1, 2, 3; 4, 5, 6];
  B = A(A > 2) % Find all elements in A greater than 2
  

• Anonymous Functions: Quickly create simple functions.

  f = @(x) x.^2 + 2*x + 1; % Define an anonymous function f(x) = x^2 + 2x + 1
  result = f(3)