MATLAB-Fortran Mixed Programming via MEX Interface

MEX-based MATLAB-Fortran mixed programming enables calling high-performance Fortran code within the MATLAB environment, particularly suitable for numerical computation-intensive tasks. Below are implementation steps and key concepts:

### 1. Environment Setup Ensure MATLAB supports MEX compilation (requires installation of compatible MATLAB compiler). Install a compatible Fortran compiler (such as Intel Fortran or GFortran) and configure it in MATLAB using the `mex -setup` command.

### 2. Fortran Code Development Fortran subroutines must adhere to MEX interface specifications, including the `mexFunction` entry point for handling data exchange between MATLAB and Fortran. Use `%val` or pointer handling for dynamic arrays, noting that MATLAB arrays are column-major by default (Fortran also uses column-major order, eliminating transpose requirements).

### 3. MEX File Compilation Compile Fortran source code using the `mex` command, for example: mex -v my_fortran_code.f This generates binary files with `.mex` extensions (such as `.mexa64` or `.mexw64`).

### 4. Data Interaction Considerations Input/Output: Access MATLAB arrays through `mxGetPr` (get real part pointer) and `mxGetPi` (get imaginary part pointer). Memory Management: Avoid dynamic memory allocation in Fortran; let MATLAB control memory lifecycle. Type Matching: Explicitly handle correspondence between double precision (`real*8`) and MATLAB's default `double` type.

### 5. Debugging and Optimization Error Checking: Use `mexErrMsgTxt` in Fortran to return error messages. Performance: Leverage Fortran's vectorized operations to reduce loops, and validate speed improvements using MATLAB's `tic/toc` functions.

### Typical Application Scenarios Accelerate core computational components of existing MATLAB algorithms (e.g., matrix decomposition, PDE solvers). Reuse legacy Fortran scientific computing libraries without rewriting them as MATLAB code.

Through the MEX interface, MATLAB seamlessly integrates Fortran's efficient computational capabilities while retaining its own usability and visualization advantages.