diff --git a/lectures/_toc.yml b/lectures/_toc.yml
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@@ -67,6 +67,7 @@ parts:
   - file: dyn_stack
   - file: calvo_machine_learn
   - file: calvo
+  - file: calvo_addon
   - file: opt_tax_recur
   - file: amss
   - file: amss2
diff --git a/lectures/calvo_addon.md b/lectures/calvo_addon.md
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+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.16.1
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Another Look at Machine Learning a Ramsey Plan
+
+I'd like  you  to use your gradient code to compute a Ramsey plan for a versionof Calvo's original model.
+
+That model was not LQ.
+
+But your code can be tweaked to compute the Ramsey plan I suspect.
+
+I sketch the main idea here.
+
+## Calvo's setup
+
+The Ramsey planner's one-period social utility function
+is 
+
+$$
+u(c) + v(m) \equiv u(f(x)) + v(m)
+$$
+
+where  in our notation Calvo or Chang's objects  become
+
+$$ 
+\begin{align*}
+x & = \mu \cr 
+m & = -\alpha \theta \cr 
+u(c) & = u(f(x)) = u(f(\mu)) \cr 
+v(m) & = v (-\alpha \theta)
+\end{align*}
+$$
+
+
+In the quantecon lecture about the Chang-Calvo model, we deployed the following functional forms:
+
+$$
+u(c) = \log(c)
+$$
+
+$$
+v(m) = \frac{1}{500}(m \bar m - 0.5m^2)^{0.5}
+$$
+
+$$
+f(x) = 180 - (0.4x)^2
+$$
+
+where $\bar m$ is a parameter set somewhere in the quantecon code.
+
+So with this parameterization of Calvo and Chang's functions, components of  our one-period criterion  become
+
+$$
+u(c_t) = \log (180 - (0.4 \mu_t)^2) 
+$$
+
+and
+
+$$
+v(m_t - p_t) = \frac{1}{500}((-\alpha \theta_t)  \bar m - 0.5(-\alpha \theta_t)^2)^{0.5}
+$$
+
+As in our ``calvo_machine_learning`` lecture, the Ramsey planner maximizes the criterion
+
+$$
+\sum_{t=0}^\infty \beta^t [ u(c_t) + v(m_t)] \tag{1}
+$$
+
+subject to the constraint 
+
+
+$$
+\theta_t = \frac{1}{1+\alpha} \sum_{j=0}^\infty \left(\frac{\alpha}{1+\alpha}\right)^j \mu_{t+j}, \quad t \geq 0 \tag{2}
+$$
+
+
+## Proposal to Humphrey
+
+I'd like to take big parts of the code that you used for the ``gradient`` method only -- the first method you created -- and adapt it to compute a Ramsey plan for this non LQ version of the Calvo's model.
+
+  * it is quite close to the version in the main part of Calvo's 1978 classic paper
+  
+I'd like you to use exactly the same assumptions about the $\{\mu_t\}_{t=0}^\infty$ process that is in the code, which means that you'll only have to compute a truncated sequence $\{\mu_t\}_{t=0}^T$ parameterized by   $T$ and $\bar \mu$ as in the code.
+
+And it would be great if you could also compute the Ramsey plan restricted to a constant $\vec \mu$ sequence so that we could get our hands on that plan to plot and compare with the ordinary Ramsey plan. 
+
+
+After you've computed the Ramsey plan, I'd like to ask you to plot the pretty graphs of $\vec \theta, \vec \mu$ and also $\vec v$ where (recycling notation) here $\vec v$ is a sequence of continuation values.
+
+Qualitatively, these graphs should look a lot like the ones plotted in the ``calvo_machine_learning`` lecture.  
+
+Later, after those plots are done, I'd like to describe some **nonlinear** regressions to run that we'll use to try to discover a recursive represenation of a Ramsey plan.  
+
+  * this will be fun -- we might want to use some neural nets to approximate these functions -- then we'll **really** be doing machine learning. 
+  
+
+## Excuses and Apologies
+
+I have recycled some notation -- e.g., $v$ and $v_t$.  And Calvo uses $m$ for what we call $m_t - p_t$ and so on. 
+
+Later we can do some notation policing and cleaning up.
+
+But right now, let's just proceed as best we can with notation that makes it easiest to take
+the ``calvo_machine_learning`` code and apply it with minimal changes. 
+
+Thanks!
+
+```{code-cell} ipython3
+from quantecon import LQ
+import numpy as np
+import jax.numpy as jnp
+from jax import jit, grad
+import optax
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+```
+
+```{code-cell} ipython3
+from collections import namedtuple
+
+def create_ChangML(T=40, β=0.3, c=2, α=1, mbar=30.0):
+    ChangML = namedtuple('ChangML', ['T', 'β', 'c', 'α', 
+                                     'mbar'])
+
+    return ChangML(T=T, β=β, c=c, α=α, mbar=mbar)
+
+
+model = create_ChangML()
+```
+
+```{code-cell} ipython3
+@jit
+def compute_θ(μ, α):
+    λ = α / (1 + α)
+    T = len(μ) - 1
+    μbar = μ[-1]
+    
+    # Create an array of powers for λ
+    λ_powers = λ ** jnp.arange(T + 1)
+    
+    # Compute the weighted sums for all t
+    weighted_sums = jnp.array(
+        [jnp.sum(λ_powers[:T-t] * μ[t:T]) for t in range(T)])
+    
+    # Compute θ values except for the last element
+    θ = (1 - λ) * weighted_sums + λ**(T - jnp.arange(T)) * μbar
+    
+    # Set the last element
+    θ = jnp.append(θ, μbar)
+    
+    return θ
+
+def compute_V(μ, model):
+    β, c, α, mbar,= model.β, model.c, model.α, model.mbar
+    θ = compute_θ(μ, α)
+    
+    T = len(μ) - 1
+    t = jnp.arange(T)
+    
+    def u(μ, m, mbar): 
+        # print('μ', μ)
+        f_μ = 180 - (0.4 * μ)**2
+        # print('f_μ', f_μ)
+        # print('m, mbar:', m, mbar, (mbar * m - 0.5 * m**2))
+        v_m = 1 / 500 * jnp.sqrt(jnp.maximum(1e-16, mbar * m - 0.5 * m**2))
+        # print('v_μ', v_m)
+        return jnp.log(f_μ) + v_m
+    
+    # Compute sum except for the last element
+    
+    # TO TOM: -α*θ is causing trouble in utility calculation (u) above
+    # As it makes mbar * m - 0.5 * m**2 < 0 and sqrt of negative
+    # values returns NA
+    V_sum = jnp.sum(β**t * u(μ[:T], -α*θ[:T], mbar))
+    # print('V_sum', V_sum)
+    
+    # Compute the final term
+    V_final = (β**T / (1 - β)) * u(μ[-1], -α*θ[-1], mbar)
+    # print('V_final', V_final)
+    V = V_sum + V_final
+    
+    return V
+
+# compute_V(jnp.array([1.0, 1.0, 1.0]), model)
+```
+
+```{code-cell} ipython3
+def adam_optimizer(grad_func, init_params, 
+                   lr=0.1, 
+                   max_iter=10_000, 
+                   error_tol=1e-7):
+
+    # Set initial parameters and optimizer
+    params = init_params
+    optimizer = optax.adam(learning_rate=lr)
+    opt_state = optimizer.init(params)
+
+    # Update parameters and gradients
+    @jit
+    def update(params, opt_state):
+        grads = grad_func(params)
+        updates, opt_state = optimizer.update(grads, opt_state)
+        params = optax.apply_updates(params, updates)
+        return params, opt_state, grads
+
+    # Gradient descent loop
+    for i in range(max_iter):
+        params, opt_state, grads = update(params, opt_state)
+        
+        if jnp.linalg.norm(grads) < error_tol:
+            print(f"Converged after {i} iterations.")
+            break
+
+        if i % 100 == 0: 
+            print(f"Iteration {i}, grad norm: {jnp.linalg.norm(grads)}")
+    
+    return params
+```
+
+```{code-cell} ipython3
+T = 40 
+
+# Initial guess for μ
+μ_init = jnp.ones(T)
+
+# Maximization instead of minimization
+grad_V = jit(grad(
+    lambda μ: -compute_V(μ, model)))
+```
+
+```{code-cell} ipython3
+%%time
+
+# Optimize μ
+optimized_μ = adam_optimizer(grad_V, μ_init)
+
+print(f"optimized μ = \n{optimized_μ}")
+```
+
+```{code-cell} ipython3
+compute_V(optimized_μ, model)
+```
+
+```{code-cell} ipython3
+model = create_ChangML(β=0.8)
+
+grad_V = jit(grad(
+    lambda μ: -compute_V(μ, model)))
+```
+
+```{code-cell} ipython3
+%%time
+
+# Optimize μ
+optimized_μ = adam_optimizer(grad_V, μ_init)
+
+print(f"optimized μ = \n{optimized_μ}")
+```
+
+```{code-cell} ipython3
+compute_V(optimized_μ, model)
+```