7 docs tagged with "basics"

Compute derivatives, gradients, Jacobians, Hessians, JVPs, and VJPs with Gradgen's forward and reverse automatic differentiation tools.

Define symbolic functions with scalar and vector inputs, named arguments, composition, and multi-output evaluation in Gradgen.

Build batched map, zip, and reduce kernels in Gradgen to exploit structure and generate smaller Rust code.

Model and differentiate single-shooting optimal control problems in Gradgen, including stage costs, terminal costs, gradients, and Hessian-vector products.

Generate embeddable Rust code from symbolic Gradgen functions, including no_std kernels, workspace slices, and derivative bundles.

Expose generated Rust crates to Python with Gradgen's optional Python bridge, workspace helpers, and metadata inspection APIs.

Create scalar and vector symbolic expressions in Gradgen, including slicing, constants, vector construction, and quadratic forms.