Remove solution; revert later after going over the topic

notes-and-examples/2026.08.20/exercise2.py

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+# See the overview from 2026.07.31.
+# Note: I added Anaheim to the sample graph here. The solution should work
+# appropriately with either graph.
+
+sample_graph = {
+    'Santa Ana': {
+        'Los Angeles': 5,
+        'Anaheim': 3,
+        'Palm Springs': 50
+    },
+    'Anaheim': {
+        'Los Angeles': 2,
+        'Santa Ana': 3
+    },
+    'Los Angeles': {
+        'Anaheim': 2,
+        'San Francisco': 25,
+        'Santa Ana': 5
+    },
+    'Palm Springs': {
+        'San Francisco': 30,
+        'Santa Ana': 50
+    },
+    'San Francisco': {
+        'Los Angeles': 25,
+        'Palm Springs': 30
+    }
+}
+
+def possible_paths(start: str, end: str):
+    # TODO
+    pass
+
+print(possible_paths('Santa Ana', 'San Francisco'))

notes-and-examples/2026.08.20/overview.md

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+## Outline
+
+We're working on exercise 2 from [2026.07.31](../2026.07.31/overview.md).
+The exercise and starter file is included again below for convenience.
+
+In a *weighted* road network, return all possible paths that a car can take to
+reach point A to point B. For each path, also sum up the total weight that
+taking that path requires.
+
+Your algorithm should take the starting and ending points, and return a list
+of tuples. Each tuple should contain the traversal from point A to point B,
+followed by the total weight.
+
+In this fictitious graph, calling `possible_paths("Santa Ana", "San Francisco")`
+should yield the return below.
+
+```python
+graph = {
+    'Santa Ana': {
+        'Los Angeles': 5,
+        'Palm Springs': 50
+    },
+    'Los Angeles': {
+        'San Francisco': 25,
+        'Santa Ana': 5
+    },
+    'Palm Springs': {
+        'San Francisco': 30,
+        'Santa Ana': 50
+    },
+    'San Francisco': {
+        'Los Angeles': 25,
+        'Palm Springs': 30
+    } 
+}
+```
+
+```
+[
+    (
+        ["Santa Ana", "Los Angeles", "San Francisco"],
+        30
+    ),
+    (
+        ["Santa Ana", "Palm Springs", "San Francisco"],
+        80
+    )
+]
+```

notes-and-examples/2026.08.27/topological_sort.py

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+from collections import deque
+# double-ended queue, this may be helpful
+# see the docs: https://docs.python.org/3/library/collections.html#collections.deque
+
+classes = {
+    'CPSC 230': {'CPSC 231'},
+    'CPSC 231': {'CPSC 350', 'CPSC 330'},
+    'CPSC 350': {'CPSC 380', 'CPSC 408', 'CPSC 406'},
+    'CPSC 330': {'CPSC 351'},
+    'CPSC 351': set(),
+    'ENGR 101': set(),
+    'CPSC 380': set(),
+    'CPSC 406': set(),
+    'CPSC 408': set()
+}
+
+def topological_sort(adjacency_list: dict[str, set[str]]):
+    # return the sorted ordering
+    return []
+
+print(topological_sort(classes))