Fix convergence conditions

Author: Smit-create

lectures/_static/lecture_specific/coleman_policy_iter/solve_time_iter.py

@@ -17,10 +17,10 @@ def solve_model_time_iter(model,    # Class with model information
             print(f"Error at iteration {i} is {error}.")
         σ = σ_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
     return σ_new

lectures/_static/lecture_specific/optgrowth/solve_model.py

@@ -21,10 +21,10 @@ def solve_model(og,
             print(f"Error at iteration {i} is {error}.")
         v = v_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
     return v_greedy, v_new

lectures/cake_eating_numerical.md

@@ -88,11 +88,11 @@ The basic idea is:
 
 1. Take an arbitary intial guess of $v$.
 1. Obtain an update $w$ defined by
-   
+
    $$
    w(x) = \max_{0\leq c \leq x} \{u(c) + \beta v(x-c)\}
    $$
-   
+
 1. Stop if $w$ is approximately equal to $v$, otherwise set
    $v=w$ and go back to step 2.
 
@@ -299,10 +299,10 @@ def compute_value_function(ce,
 
         v = v_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
     return v_new
@@ -657,10 +657,10 @@ def iterate_euler_equation(ce,
 
         σ = σ_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
     return σ
@@ -685,4 +685,4 @@ plt.show()
 ```
 
 ```{solution-end}
-```
+```

lectures/career.md

@@ -300,12 +300,11 @@ def solve_model(cw,
             print(f"Error at iteration {i} is {error}.")
         v = v_new
 
-    if i == max_iter and error > tol:
+    if error > tol:
         print("Failed to converge!")
 
-    else:
-        if verbose:
-            print(f"\nConverged in {i} iterations.")
+    elif verbose:
+        print(f"\nConverged in {i} iterations.")
 
     return v_new
 ```
@@ -545,4 +544,4 @@ has become more concentrated around the mean, making high-paying jobs
 less realistic.
 
 ```{solution-end}
-```
+```

lectures/ifp_advanced.md

@@ -494,10 +494,10 @@ def solve_model_time_iter(model,        # Class with model information
             print(f"Error at iteration {i} is {error}.")
         a_vec, σ_vec = np.copy(a_new), np.copy(σ_new)
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
     return a_new, σ_new

lectures/jv.md

@@ -362,10 +362,10 @@ def solve_model(jv,
             print(f"Error at iteration {i} is {error}.")
         v = v_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
     return v_new
@@ -569,4 +569,4 @@ This seems reasonable and helps us confirm that our dynamic programming
 solutions are probably correct.
 
 ```{solution-end}
-```
+```

lectures/mccall_correlated.md

@@ -281,10 +281,10 @@ def compute_fixed_point(js,
             print(f"Error at iteration {i} is {error}.")
         f_in[:] = f_out
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
     return f_out
@@ -453,4 +453,4 @@ plt.show()
 The figure shows that more patient individuals tend to wait longer before accepting an offer.
 
 ```{solution-end}
-```
+```

lectures/navy_captain.md

@@ -538,7 +538,7 @@ def solve_model(wf, tol=1e-4, max_iter=1000):
         i += 1
         h = h_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
     return h_new
@@ -621,25 +621,25 @@ conditioning on knowing for sure that nature has selected $f_{0}$,
 in the first case, or $f_{1}$, in the second case.
 
 1. under $f_{0}$,
-   
+
    $$
    V^{0}\left(\pi\right)=\begin{cases}
    0 & \text{if }\alpha\leq\pi,\\
    c+EV^{0}\left(\pi^{\prime}\right) & \text{if }\beta\leq\pi<\alpha,\\
    \bar L_{1} & \text{if }\pi<\beta.
    \end{cases}
    $$
-   
+
 1. under $f_{1}$
-   
+
    $$
    V^{1}\left(\pi\right)=\begin{cases}
    \bar L_{0} & \text{if }\alpha\leq\pi,\\
    c+EV^{1}\left(\pi^{\prime}\right) & \text{if }\beta\leq\pi<\alpha,\\
    0 & \text{if }\pi<\beta.
    \end{cases}
    $$
-   
+
 
 where
 $\pi^{\prime}=\frac{\pi f_{0}\left(z^{\prime}\right)}{\pi f_{0}\left(z^{\prime}\right)+\left(1-\pi\right)f_{1}\left(z^{\prime}\right)}$.
@@ -1118,4 +1118,3 @@ plt.title('Uncond. distribution of log likelihood ratio at frequentist  t')
 
 plt.show()
 ```
-

lectures/odu.md

@@ -149,10 +149,10 @@ $$
 
 
 
-The  worker's time $t$ subjective belief about the  the distribution of $W_t$  is 
+The  worker's time $t$ subjective belief about the  the distribution of $W_t$  is
 
 $$
-\pi_t f + (1 - \pi_t) g, 
+\pi_t f + (1 - \pi_t) g,
 $$
 
 where $\pi_t$ updates via
@@ -427,10 +427,10 @@ def solve_model(sp,
             print(f"Error at iteration {i} is {error}.")
         v = v_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
 
@@ -731,10 +731,10 @@ def solve_wbar(sp,
             print(f"Error at iteration {i} is {error}.")
         w = w_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
-    if verbose and i < max_iter:
+    if verbose:
         print(f"\nConverged in {i} iterations.")
 
     return w_new
@@ -1178,4 +1178,3 @@ after having acquired less information about the wage distribution.
 ```{code-cell} python3
 job_search_example(1, 1, 3, 1.2, c=0.1)
 ```
-

lectures/wald_friedman.md

@@ -526,7 +526,7 @@ def solve_model(wf, tol=1e-4, max_iter=1000):
         i += 1
         h = h_new
 
-    if i == max_iter:
+    if error > tol:
         print("Failed to converge!")
 
     return h_new
@@ -902,11 +902,11 @@ Wald summarizes Neyman and Pearson's setup as follows:
 
 > Neyman and Pearson show that a region consisting of all samples
 > $(z_1, z_2, \ldots, z_n)$ which satisfy the inequality
-> 
+>
 > $$
   \frac{ f_1(z_1) \cdots f_1(z_n)}{f_0(z_1) \cdots f_0(z_n)} \geq k
   $$
-> 
+>
 > is a most powerful critical region for testing the hypothesis
 > $H_0$ against the alternative hypothesis $H_1$. The term
 > $k$ on the right side is a constant chosen so that the region