autograd-cpp

A lightweight, high-performance C++ automatic differentiation library using computational graphs.

Features

• Computational Graph-based AD: Forward and backward propagation through dynamic graphs
• Jacobian & Hessian: First and second-order derivative computations
• Optimizers: SGD with learning rate scheduling (linear, exponential, cosine, polynomial)
• Header-mostly: Minimal dependencies, easy integration
• CMake Package: FetchContent support for seamless integration

Quick Start

Using in Your Project (CMake FetchContent)

include(FetchContent)

FetchContent_Declare(
    autograd_cpp
    GIT_REPOSITORY https://github.com/queelius/autograd-cpp.git
    GIT_TAG main
)

set(BUILD_EXAMPLES OFF CACHE BOOL "" FORCE)
set(BUILD_TESTS OFF CACHE BOOL "" FORCE)

FetchContent_MakeAvailable(autograd_cpp)

target_link_libraries(your_app PRIVATE autograd::autograd)

Example Code

#include <autograd/autograd.hpp>

using namespace autograd;

int main() {
    // Create computation graph
    auto x = constant(3.0);
    auto y = constant(4.0);
    auto z = mul(x, y);        // z = x * y
    auto result = add(z, constant(2.0));  // result = z + 2

    // Compute gradients
    result->backward();

    std::cout << "Result: " << result->data[0] << std::endl;  // 14
    std::cout << "dz/dx: " << x->grad[0] << std::endl;        // 4
    std::cout << "dz/dy: " << y->grad[0] << std::endl;        // 3

    return 0;
}

Core Components

• tensor.hpp: Tensor class with gradient tracking
• ops.hpp: Operations (add, mul, exp, log, matmul, etc.)
• jacobian.hpp: Jacobian matrix computation
• hessian.hpp: Hessian matrix computation
• optim.hpp: SGD optimizer with learning rate schedules

Building Examples

git clone https://github.com/queelius/autograd-cpp.git
cd autograd-cpp
mkdir build && cd build
cmake ..
make -j$(nproc)

# Run examples
./examples/simple_gradients
./examples/hessian_demo

Requirements

• C++17 or later
• CMake 3.14+
• Optional: OpenMP for parallelization

Use Cases

This library provides the core automatic differentiation engine. Build on top of it for:

• Neural networks and deep learning
• Statistical modeling and inference
• Physics simulations requiring gradients
• Optimization algorithms
• Scientific computing

Design Philosophy

autograd-cpp is designed to be:

• Minimal: Core AD functionality only - build your domain-specific features on top
• Efficient: Optimized for performance with optional OpenMP parallelization
• Flexible: Dynamic computational graphs for arbitrary computations
• Portable: Standard C++17, works on any platform

License

[Specify your license]

Contributing

Contributions welcome! This is the core AD engine - domain-specific extensions (neural networks, statistical models, etc.) should be separate packages that depend on autograd-cpp.