What Every Beginner Should Know About MATLAB ?

In 1983, Stanford PhD Jack Little, experimenting with MATLAB for control theory applications, partnered with DEC engineer Steve Bangert. Together with Cleve Moler, they rewrote MATLAB’s core engine in C language, releasing MATLAB 1.0 in late 1984 as a highly portable ~150KB executable that ran on Unix, VMS, Mac, and DOS platforms. MathWorks Inc. officially launched on December 7, 1984, in Natick, Massachusetts, with Jack Little as CEO, Cleve Moler as CTO, and Steve Bangert as VP of Engineering. The company’s first sale came in February 1985 to Nick Trefethen at MIT Lincoln Lab for $500 on floppy disks, targeting control systems engineers. Early revenue accelerated with specialized toolboxes: the Control System Toolbox (developed by Jack Little) and Signal Processing Toolbox (with Stanford’s Tom Kailath, released 1986). This ecosystem approach fueled MATLAB’s rapid adoption in engineering workflows, including Simulink precursors and embedded systems development.

Growth Through Versions 2-4 (1986-1992)

MATLAB Version 2 (1986) introduced essential plotting functions like PLOT and PRINT, plus MAT-files for saving workspaces—making data visualization and persistence accessible to control engineers and researchers. Version 3 (1987) brought M-files (scripts and functions), 3D graphics, and the Handle Graphics object system, expanding platform support to Sun-3, HP Apollo, and PCs. By Version 3.5 (1989), sparse matrix support and ~20 toolboxes established MATLAB’s competitive edge over newcomers like Mathematica (1988), particularly in numerical computing and signal processing. Version 4 (1992) added structures, cell arrays, early notebook-style evaluation (Live Editor precursors), and object-oriented programming foundations, while supporting Windows 3.1. With thousands of users and MathWorks staff growing to 30 employees (plus a 1991 European office), MATLAB became the go-to platform for engineering workflows including Simulink development and academic research.

Simulink Era and Boom (1990s)

Simulink 1.0 Launch and MATLAB R10-R12 Milestones Simulink 1.0 (1990) revolutionized engineering with its block-diagram GUI for dynamic systems simulation, directly leveraging MATLAB’s numerical solvers—a game-changer for control systems, aerospace, and early model-based design workflows. MATLAB R10/V5 (1997) delivered a major syntax overhaul, making structures and cell arrays native, embedding the Java VM, and enhancing the ODE suite; MATLAB 5.3 introduced the compiler for standalone deployment. The 1990s saw explosive toolbox growth—Image Processing (1993), Optimization (1994), Statistics (1996), Wavelet (1998)—reaching over 50 toolboxes by 1999 for specialized domains like signal processing and automotive applications. R11/V6 (1998) doubled performance with JIT compiler hints, while R12 (2000) added the modern workspace browser and full object-oriented classes. MathWorks revenue surpassed $100M, 

2000s: Enterprise and Performance

R13 to R2010b: Notebooks, Parallel Computing & GPU Acceleration

MATLAB R13 (2002) introduced the notebook interface and web deployment features, enabling interactive MATLAB notebooks and collaborative engineering workflows. R2006a added Unicode support and 64-bit computing for handling larger datasets, while the Parallel Computing Toolbox (2008) and GPU arrays (R2010b) delivered CUDA acceleration—perfect for high-performance tasks like real-time noise cancellation algorithms and signal processing in embedded systems. R2007a advanced the Live Editor for rich, interactive documentation, and R2009a launched Simulink Control Design for automated controller tuning in control systems. MATLAB user licenses surpassed 1 million, strengthened by strategic acquisitions like PolySpace (2009) for static code analysis in safety-critical AUTOSAR and aerospace applications. R2010b enhanced string functions and previewed tall arrays for big data processing, cementing MATLAB’s dominance in model-based design, Simulink code generation, and modern engineering workflows for automotive developers.

R-Series Maturity (2010s)

R2012a to R2019b: Modern IDE, Deep Learning & Embedded Coder Advancements MATLAB R2012a (v8) introduced the tabbed desktop, App Designer, and web app deployment—ushering in a fully modern MATLAB IDE for streamlined engineering development. R2014b made 64-bit support default with Git integration for version control, while R2016a launched Live Scripts and established the annual release cycle. Key additions included Deep Learning Toolbox (R2017b) and Predictive Maintenance Toolbox (R2017b), expanding into AI and industrial applications. R2018b introduced string arrays (replacing char arrays) for cleaner code, and R2019b added Reinforcement Learning Toolbox for advanced control systems. By 2019, MATLAB served 4+ million users with over 100 toolboxes, featuring critical Embedded Coder enhancements for AUTOSAR compliance and Simulink code generation in automotive embedded systems.

2020s: AI, Cloud, Interop

R2020a to R2026a: Cloud, AI, Edge Computing & Embedded Integration The COVID-19 pandemic accelerated MATLAB Online adoption as a fully browser-based platform, enabling seamless remote collaboration for engineering teams worldwide. R2020a introduced Live Functions for interactive development, while R2021a added ARM architecture support—crucial for embedded devices. R2022b brought pattern matching syntax and GPU Coder for deploying deep learning models to edge AI hardware. R2024a/b launched LLM-powered apps for automated code generation (similar to ChatGPT integration) and AutoML GUI for accessible machine learning, while R2025a/b introduced the Quantum Computing Toolbox and Rust interoperability—vital for high-performance Arduino projects and modern embedded systems workflows. Python integration became seamless with pyrun and package manager support, plus Production Server for scalable deployments. As of January 2026, R2026a previews edge AI advancements; MathWorks employs ~6,000 staff, generates $1.6B revenue, serves 5M+ users, with the Bengaluru office strengthening local AUTOSAR, Simulink, and automotive engineering ecosystems.

Technical Evolution Highlights

MATLAB’s numerical solvers evolved from ode45 (introduced 1996 for non-stiff ODEs) to comprehensive implicit and explicit solver suites (2016), powering precise simulations in control systems, Simulink models, and embedded applications. Graphics systems progressed from Handle Graphics 1.0 (HG1) (1987 basic 2D/3D plotting) to the modern HG2 rendering engine (R2014b), delivering high-performance, customizable visualizations for engineering data analysis and technical presentations. Data handling advanced with table data types (R2013b) for spreadsheet-like operations, tall arrays (R2016b) for out-of-memory big data, and datastores for scalable file-based workflows—essential for processing large datasets in automotive signal analysis. Deployment options expanded from MEX files (C/Fortran integration) and MATLAB Compiler (1999) to specialized Coder suites (C/C++, HDL), plus cutting-edge WebAssembly support (R2023b) for browser-based MATLAB apps and edge device deployment.

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Global Impact

MATLAB powers mission-critical applications like Mars rovers navigation, COVID-19 epidemiological models, EV battery management systems (leveraging Simulink for model-based design), and even Oscar-winning VFX in Hollywood films—demonstrating its unmatched versatility across engineering and science. It directly inspired open-source ecosystems like NumPy and SciPy, while free alternatives such as GNU Octave emerged but struggle to match MATLAB’s comprehensive toolbox ecosystem and enterprise support for automotive and embedded workflows. MathWorks invests over $1B annually in R&D, offers free student licenses, and delivers specialized tools perfectly suited for Simulink/AUTOSAR compliance in automotive development, DSP Toolbox for real-time noise cancellation algorithms, and App Designer for creating interactive YouTube tutorials and technical content.