Add bottom-up dynamic programming solution to Jump Game.

src/algorithms/uncategorized/jump-game/__test__/dpBottomUpJumpGame.test.js

@@ -0,0 +1,17 @@
+import dpBottomUpJumpGame from '../dpBottomUpJumpGame';
+
+describe('dpBottomUpJumpGame', () => {
+  it('should solve Jump Game problem in bottom-up dynamic programming manner', () => {
+    expect(dpBottomUpJumpGame([1, 0])).toBeTruthy();
+    expect(dpBottomUpJumpGame([100, 0])).toBeTruthy();
+    expect(dpBottomUpJumpGame([2, 3, 1, 1, 4])).toBeTruthy();
+    expect(dpBottomUpJumpGame([1, 1, 1, 1, 1])).toBeTruthy();
+    expect(dpBottomUpJumpGame([1, 1, 1, 10, 1])).toBeTruthy();
+    expect(dpBottomUpJumpGame([1, 5, 2, 1, 0, 2, 0])).toBeTruthy();
+
+    expect(dpBottomUpJumpGame([1, 0, 1])).toBeFalsy();
+    expect(dpBottomUpJumpGame([3, 2, 1, 0, 4])).toBeFalsy();
+    expect(dpBottomUpJumpGame([0, 0, 0, 0, 0])).toBeFalsy();
+    expect(dpBottomUpJumpGame([5, 4, 3, 2, 1, 0, 0])).toBeFalsy();
+  });
+});

src/algorithms/uncategorized/jump-game/backtrackingJumpGame.js

@@ -1,6 +1,12 @@
 /**
  * BACKTRACKING approach of solving Jump Game.
  *
+ * This is the inefficient solution where we try every single jump
+ * pattern that takes us from the first position to the last.
+ * We start from the first position and jump to every index that
+ * is reachable. We repeat the process until last index is reached.
+ * When stuck, backtrack.
+ *
  * @param {number[]} numbers - array of possible jump length.
  * @param {number} startIndex - index from where we start jumping.
  * @param {number[]} currentJumps - current jumps path.

src/algorithms/uncategorized/jump-game/dpBottomUpJumpGame.js

@@ -0,0 +1,46 @@
+/**
+ * DYNAMIC PROGRAMMING BOTTOM-UP approach of solving Jump Game.
+ *
+ * This comes out as an optimisation of DYNAMIC PROGRAMMING TOP-DOWN approach.
+ *
+ * The observation to make here is that we only ever jump to the right.
+ * This means that if we start from the right of the array, every time we
+ * will query a position to our right, that position has already be
+ * determined as being GOOD or BAD. This means we don't need to recurse
+ * anymore, as we will always hit the memo table.
+ *
+ * We call a position in the array a "good" one if starting at that
+ * position, we can reach the last index. Otherwise, that index
+ * is called a "bad" one.
+ *
+ * @param {number[]} numbers - array of possible jump length.
+ * @return {boolean}
+ */
+export default function dpBottomUpJumpGame(numbers) {
+  // Init cells goodness table.
+  const cellsGoodness = Array(numbers.length).fill(undefined);
+  // Mark the last cell as "good" one since it is where we ultimately want to get.
+  cellsGoodness[cellsGoodness.length - 1] = true;
+
+  // Go throw all cells starting from the one before the last
+  // one (since the last one is "good" already) and fill cellsGoodness table.
+  for (let cellIndex = numbers.length - 2; cellIndex >= 0; cellIndex -= 1) {
+    const maxJumpLength = Math.min(
+      numbers[cellIndex],
+      numbers.length - 1 - cellIndex,
+    );
+
+    for (let jumpLength = maxJumpLength; jumpLength > 0; jumpLength -= 1) {
+      const nextIndex = cellIndex + jumpLength;
+      if (cellsGoodness[nextIndex] === true) {
+        cellsGoodness[cellIndex] = true;
+        // Once we detected that current cell is good one we don't need to
+        // do further cells checking.
+        break;
+      }
+    }
+  }
+
+  // Now, if the zero's cell is good one then we can jump from it to the end of the array.
+  return cellsGoodness[0] === true;
+}

src/algorithms/uncategorized/jump-game/dpTopDownJumpGame.js

@@ -2,11 +2,14 @@
  * DYNAMIC PROGRAMMING TOP-DOWN approach of solving Jump Game.
  *
  * This comes out as an optimisation of BACKTRACKING approach.
- * Optimisation is done by using memo table where we store information
- * about each cell whether it is "good" or "bad" or "unknown".
+ *
+ * It relies on the observation that once we determine that a certain
+ * index is good / bad, this result will never change. This means that
+ * we can store the result and not need to recompute it every time.
  *
  * We call a position in the array a "good" one if starting at that
- * position, we can reach the last index.
+ * position, we can reach the last index. Otherwise, that index
+ * is called a "bad" one.
  *
  * @param {number[]} numbers - array of possible jump length.
  * @param {number} startIndex - index from where we start jumping.
@@ -30,8 +33,7 @@ export default function dpTopDownJumpGame(
   const currentCellsGoodness = [...cellsGoodness];
   if (!currentCellsGoodness.length) {
     numbers.forEach(() => currentCellsGoodness.push(undefined));
-    // Mark the last cell as "good" one since it is where
-    // we ultimately want to get.
+    // Mark the last cell as "good" one since it is where we ultimately want to get.
     currentCellsGoodness[cellsGoodness.length - 1] = true;
   }
 

src/algorithms/uncategorized/jump-game/greedyJumpGame.js

@@ -1,6 +1,19 @@
 /**
  * GREEDY approach of solving Jump Game.
  *
+ * This comes out as an optimisation of DYNAMIC PROGRAMMING BOTTOM_UP approach.
+ *
+ * Once we have our code in the bottom-up state, we can make one final,
+ * important observation. From a given position, when we try to see if
+ * we can jump to a GOOD position, we only ever use one - the first one.
+ * In other words, the left-most one. If we keep track of this left-most
+ * GOOD position as a separate variable, we can avoid searching for it
+ * in the array. Not only that, but we can stop using the array altogether.
+ *
+ * We call a position in the array a "good" one if starting at that
+ * position, we can reach the last index. Otherwise, that index
+ * is called a "bad" one.
+ *
  * @param {number[]} numbers - array of possible jump length.
  * @return {boolean}
  */