Start documentation for projections.

docs/api/projections.rst

@@ -0,0 +1,48 @@
+Projections
+===========
+
+.. currentmodule:: optax.projections
+
+Projections can be used to perform constrained optimization.
+The Euclidean projection onto a set :math:`\mathcal{C}` is:
+
+.. math::
+
+    \text{proj}_{\mathcal{C}}(u) :=
+    \underset{v}{\text{argmin}} ~ ||u - v||^2_2 \textrm{ subject to } v \in \mathcal{C}.
+
+For instance, here is an example how we can project parameters to the non-negative orthant::
+
+    >>> import optax
+    >>> import jax
+    >>> import jax.numpy as jnp
+    >>> num_weights = 2
+    >>> xs = jnp.array([[-1.8, 2.2], [-2.0, 1.2]])
+    >>> ys = jnp.array([0.5, 0.8])
+    >>> optimizer = optax.adam(learning_rate=1e-3)
+    >>> params = {'w': jnp.zeros(num_weights)}
+    >>> opt_state = optimizer.init(params)
+    >>> loss = lambda params, x, y: jnp.mean((params['w'].dot(x) - y) ** 2)
+    >>> grads = jax.grad(loss)(params, xs, ys)
+    >>> updates, opt_state = optimizer.update(grads, opt_state)
+    >>> params = optax.apply_updates(params, updates)
+    >>> params = optax.projections.projection_non_negative(params)
+
+Available projections
+~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+    projection_box
+    projection_hypercube
+    projection_non_negative
+
+Projection onto a box
+~~~~~~~~~~~~~~~~~~~~~
+.. autofunction:: projection_box
+
+Projection onto a hypercube
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autofunction:: projection_hypercube
+
+Projection onto the non-negative orthant
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autofunction:: projection_non_negative

docs/index.rst

@@ -60,6 +60,7 @@ for instructions on installing JAX.
    api/optimizer_wrappers
    api/optimizer_schedules
    api/apply_updates
+   api/projections
    api/losses
    api/control_variates
    api/stochastic_gradient_estimators