Switch from jaxopt to optax in relaxed rigid contact model

environment.yml

@@ -8,10 +8,10 @@ dependencies:
   - python >= 3.12.0
   - coloredlogs
   - jax >= 0.4.26
-  - jaxopt >= 0.8.0
   - jaxlib >= 0.4.26
   - jaxlie >= 1.3.0
   - jax-dataclasses >= 1.4.0
+  - optax >= 0.2.3
   - pptree
   - qpax
   - rod >= 0.3.3

pyproject.toml

@@ -45,11 +45,11 @@ classifiers = [
 dependencies = [
     "coloredlogs",
     "jax >= 0.4.26",
-    "jaxopt >= 0.8.0",
     "jaxlib >= 0.4.26",
     "jaxlie >= 1.3.0",
     "jax_dataclasses >= 1.4.0",
     "pptree",
+    "optax >= 0.2.3",
     "qpax",
     "rod >= 0.3.3",
     "typing_extensions ; python_version < '3.12'",

src/jaxsim/rbda/contacts/relaxed_rigid.py

@@ -1,12 +1,13 @@
 from __future__ import annotations
 
 import dataclasses
+from collections.abc import Callable
 from typing import Any
 
 import jax
 import jax.numpy as jnp
 import jax_dataclasses
-import jaxopt
+import optax
 
 import jaxsim.api as js
 import jaxsim.typing as jtp
@@ -297,24 +298,71 @@ def _detect_contact(x: jtp.Array, y: jtp.Array, z: jtp.Array) -> jtp.Array:
         A = G + R
         b = CW_al_free_WC - a_ref
 
-        objective = lambda x: jnp.sum(jnp.square(A @ x + b))
+        objective = lambda x, A, b: jnp.sum(jnp.square(A @ x + b))
 
-        # Compute the 3D linear force in C[W] frame
-        opt = jaxopt.LBFGS(
-            fun=objective,
-            maxiter=self.parameters.max_iterations,
-            tol=self.parameters.tolerance,
-            maxls=30,
-            history_size=10,
-            max_stepsize=100.0,
-        )
+        def run_optimization(
+            init_params: jtp.Array,
+            fun: Callable,
+            opt: optax.GradientTransformation,
+            maxiter: jtp.Int,
+            tol: jtp.Float,
+            **kwargs,
+        ):
+            value_and_grad_fn = optax.value_and_grad_from_state(fun)
+
+            def step(carry):
+                params, state = carry
+                value, grad = value_and_grad_fn(
+                    params,
+                    state=state,
+                    A=A,
+                    b=b,
+                )
+                updates, state = opt.update(
+                    updates=grad,
+                    state=state,
+                    params=params,
+                    value=value,
+                    grad=grad,
+                    value_fn=fun,
+                    A=A,
+                    b=b,
+                )
+                params = optax.apply_updates(params, updates)
+                return params, state
+
+            def continuing_criterion(carry):
+                _, state = carry
+                iter_num = optax.tree_utils.tree_get(state, "count")
+                grad = optax.tree_utils.tree_get(state, "grad")
+                err = optax.tree_utils.tree_l2_norm(grad)
+                return (iter_num == 0) | ((iter_num < maxiter) & (err >= tol))
+
+            init_carry = (init_params, opt.init(init_params))
+            final_params, final_state = jax.lax.while_loop(
+                continuing_criterion, step, init_carry
+            )
+            return final_params, final_state
 
         init_params = (
             K[:, jnp.newaxis] * jnp.zeros_like(position).at[:, 2].set(δ)
             + D[:, jnp.newaxis] * velocity
         ).flatten()
 
-        CW_f_Ci = opt.run(init_params=init_params).params.reshape(-1, 3)
+        # Compute the 3D linear force in C[W] frame
+        CW_f_Ci, _ = run_optimization(
+            init_params=init_params,
+            A=A,
+            b=b,
+            maxiter=self.parameters.max_iterations,
+            opt=optax.lbfgs(
+                memory_size=10,
+            ),
+            fun=objective,
+            tol=self.parameters.tolerance,
+        )
+
+        CW_f_Ci = CW_f_Ci.reshape((-1, 3))
 
         def mixed_to_inertial(W_H_C: jax.Array, CW_fl: jax.Array) -> jax.Array:
             W_Xf_CW = Adjoint.from_transform(