feat: add lc 46, 39, 78

apps/docs/content/backtracking/39-combination-sum.mdx

@@ -0,0 +1,53 @@
+---
+title: '39. Combination Sum'
+description: Given an array of distinct integers candidates and a target integer target, return a list of all unique combinations of candidates where the chosen numbers sum to target. You may return the combinations in any order
+sidebar:
+  label: 'Combination Sum'
+  badge: 'Medium'
+---
+
+<Badge variant="accent">Array</Badge>
+
+### Example 1:
+- Input: `candidates = [2,3,6,7], target = 7`
+- Output: `[[2,2,3],[7]]`
+- Explanation: `2` and `3` are `candidates`, and `2 + 2 + 3 = 7`. Note that `2` can be used multiple times. `7` is a candidate, and `7 = 7`. These are the only two combinations.
+
+### Example 2:
+- Input: `candidates = [2,3,5], target = 8`
+- Output: `[[2,2,2,2],[2,3,3],[3,5]]`
+
+### Example 3:
+- Input: `candidates = [2], target = 1`
+- Output: `[]`
+
+### Constraints:
+
+- `1 <= candidates.length <= 30`
+- `2 <= candidates[i] <= 40`
+- All elements of `candidates` are distinct.
+- `1 <= target <= 40`
+
+## Solution
+
+```py
+class Solution:
+    def combinationSum(self, candidates: list[int], target: int) -> list[list[int]]:
+        candidates.sort()
+        result, path = [], []
+
+        def backtrack(start, remaining):
+            if remaining == 0:
+                result.append(path[:])
+                return
+            for i in range(start, len(candidates)):
+                if candidates[i] > remaining:
+                    break
+
+                path.append(candidates[i])
+                backtrack(i, remaining - candidates[i])
+                path.pop()
+
+        backtrack(0, target)
+        return result
+```

apps/docs/content/backtracking/46-permutations.mdx

@@ -0,0 +1,52 @@
+---
+title: '46. Permutations'
+description: Given an array nums of distinct integers, return all the possible permutations. You can return the answer in any order
+sidebar:
+  label: 'Permutations'
+  badge: 'Medium'
+---
+
+<Badge variant="accent">Array</Badge>
+
+### Example 1:
+- Input: `nums = [1,2,3]`
+- Output: `[[1,2,3],[1,3,2],[2,1,3],[2,3,1],[3,1,2],[3,2,1]]`
+
+### Example 2:
+- Input: `nums = [0,1]`
+- Output: `[[0,1],[1,0]]`
+
+### Example 3:
+- Input: `nums = [1]`
+- Output: `[[1]]`
+
+### Constraints:
+
+- `1 <= nums.length <= 6`
+- `-10 <= nums[i] <= 10`
+- All the integers of `nums` are unique.
+
+## Solution
+
+```py
+class Solution:
+    def permute(self, nums: list[int]) -> list[list[int]]:
+        result, path = [], []
+        used = [False] * len(nums)
+
+        def backtrack():
+            if len(path) == len(nums):
+                result.append(path[:])
+                return
+            for i in range(len(nums)):
+                if used[i]:
+                    continue
+                used[i] = True
+                path.append(nums[i])
+                backtrack()
+                path.pop()
+                used[i] = False
+
+        backtrack()
+        return result
+```

apps/docs/content/backtracking/78-subsets.mdx

@@ -0,0 +1,41 @@
+---
+title: '78. Subsets'
+description: Given an integer array nums of unique elements, return all possible subsets (the power set)
+sidebar:
+  label: 'Subsets'
+  badge: 'Medium'
+---
+
+<Badge variant="accent">Array</Badge>
+
+### Example 1:
+- Input: `nums = [1,2,3]`
+- Output: `[[],[1],[2],[1,2],[3],[1,3],[2,3],[1,2,3]]`
+
+### Example 2:
+- Input: `nums = [0]`
+- Output: `[[],[0]]`
+
+### Constraints:
+
+- `1 <= nums.length <= 10`
+- `-10 <= nums[i] <= 10`
+- All the numbers of `nums` are unique.
+
+## Solution
+
+```py
+class Solution:
+    def subsets(self, nums: list[int]) -> list[list[int]]:
+        res = []
+
+        def backtrack(i, path):
+            res.append(path[:])
+            for num_idx in range(i, len(nums)):
+                path.append(nums[num_idx])
+                backtrack(num_idx + 1, path)
+                path.pop()
+
+        backtrack(0, [])
+        return res
+```

apps/docs/content/backtracking/index.mdx

@@ -0,0 +1,163 @@
+---
+title: Backtracking
+description: 'Backtracking is a depth-first search technique that builds a solution step by step and abandons (undoes) a partial choice as soon as it can’t lead to a valid complete solution.'
+hidden: true
+---
+
+## What it is
+
+Backtracking explores possible paths to a solution one step at a time. When a path reaches a dead end, you undo the last step and try a different option. Think of every problem as a decision tree. The root is an empty answer. Each level is a decision. Each branch is one option.
+
+## When to use it
+
+Use backtracking when you must generate **all** valid solutions or evaluate **all** combinations. Do not use it when you need only one solution or the optimal solution. For those, use greedy or dynamic programming. Backtracking has exponential time complexity.
+
+## The four components
+
+Every backtracking problem has the same parts:
+
+1. **Base case** — when to save the current path and stop.
+2. **Choices** — the options at each step.
+3. **Constraints** — the rules that reject an invalid path early.
+4. **Backtrack step** — undo the last choice and try the next one.
+
+## The template
+
+```python
+def solve(data):
+    result = []
+    path = []
+
+    def backtrack(index):
+        # 1. Base case
+        if is_complete(index, path):
+            result.append(path[:])  # copy — lists are passed by reference
+            return
+
+        # 2. Choices
+        for choice in get_choices(index, data):
+            # 3. Constraints
+            if not is_valid(choice, path):
+                continue
+
+            path.append(choice)    # make the choice
+            backtrack(index + 1)   # explore
+            path.pop()             # 4. Backtrack
+
+    backtrack(0)
+    return result
+```
+
+## Steps to apply the template
+
+1. Draw the decision tree for a small input. Name each level and each branch.
+2. Write the base case. Ask: when is the path complete?
+3. Write the choices. Ask: what can I add at this level?
+4. Write the constraints. Ask: what makes a path invalid? Reject it before recursion.
+5. Add the backtrack step. Pop what you appended.
+6. Copy the path when you save it. Never append the same list reference.
+
+## Four problems, one template
+
+### Subsets
+
+Choice: include or skip each number. No constraint. Base case: index equals list length.
+
+```python
+def subsets(nums):
+    result, path = [], []
+
+    def backtrack(i):
+        if i == len(nums):
+            result.append(path[:])
+            return
+        path.append(nums[i])   # include
+        backtrack(i + 1)
+        path.pop()
+        backtrack(i + 1)       # skip
+
+    backtrack(0)
+    return result
+```
+
+### Permutations
+
+Same choices as subsets. Constraint: do not reuse a number already in the path. Base case: path length equals list length.
+
+```python
+def permute(nums):
+    result, path = [], []
+    used = [False] * len(nums)
+
+    def backtrack():
+        if len(path) == len(nums):
+            result.append(path[:])
+            return
+        for i in range(len(nums)):
+            if used[i]:
+                continue
+            used[i] = True
+            path.append(nums[i])
+            backtrack()
+            path.pop()
+            used[i] = False
+
+    backtrack()
+    return result
+```
+
+### Combinations
+
+Same as subsets. Change one line: save the path only when its length equals `k`.
+
+```python
+def combine(n, k):
+    result, path = [], []
+
+    def backtrack(start):
+        if len(path) == k:
+            result.append(path[:])
+            return
+        for i in range(start, n + 1):
+            path.append(i)
+            backtrack(i + 1)
+            path.pop()
+
+    backtrack(1)
+    return result
+```
+
+### N-Queens
+
+Choice: which column to place the queen in the current row. Constraint: no shared column or diagonal. Base case: `n` queens placed. Backtrack: remove the last queen.
+
+```python
+def solve_n_queens(n):
+    result, cols = [], []
+    used_cols, diag1, diag2 = set(), set(), set()
+
+    def backtrack(row):
+        if row == n:
+            result.append(cols[:])
+            return
+        for col in range(n):
+            if col in used_cols or row - col in diag1 or row + col in diag2:
+                continue
+            cols.append(col)
+            used_cols.add(col); diag1.add(row - col); diag2.add(row + col)
+            backtrack(row + 1)
+            cols.pop()
+            used_cols.remove(col); diag1.remove(row - col); diag2.remove(row + col)
+
+    backtrack(0)
+    return result
+```
+
+## Common mistakes
+
+- You append the `path` list itself instead of a copy. The result fills with empty lists.
+- You forget to pop. The path grows and never resets.
+- You check constraints after recursion instead of before. You waste time on dead paths.
+- You use backtracking when you need only one answer. Use greedy or DP instead.
+
+

apps/docs/content/backtracking/meta.ts

@@ -0,0 +1,3 @@
+import { defineMeta } from "blume";
+
+export default defineMeta({ title: "Backtracking", order: 20 });