📄️ Symbolic framework
Create scalar and vector symbolic expressions in Gradgen, including slicing, constants, vector construction, and quadratic forms.
📄️ Function
Define symbolic functions with scalar and vector inputs, named arguments, composition, and multi-output evaluation in Gradgen.
📄️ Automatic differentiation
Compute derivatives, gradients, Jacobians, Hessians, JVPs, and VJPs with Gradgen's forward and reverse automatic differentiation tools.
📄️ Rust codegen
Generate embeddable Rust code from symbolic Gradgen functions, including no_std kernels, workspace slices, and derivative bundles.
📄️ Rust-Python interface
Expose generated Rust crates to Python with Gradgen's optional Python bridge, workspace helpers, and metadata inspection APIs.
📄️ Optimal control
Model and differentiate single-shooting optimal control problems in Gradgen, including stage costs, terminal costs, gradients, and Hessian-vector products.
📄️ Higher-order functions
Build batched map, zip, and reduce kernels in Gradgen to exploit structure and generate smaller Rust code.