Introduction
In the earlier post, we introduce the invariant concept to enable us to solve the binary search problem on the very first try. In this post, we further elaborate the binary search idea and introduce how we can use predicate + main theorem to solve more generalized binary search problem.
Example
Let's consider an example, which utilizes the generalized idea of binary search mentioned in TopCoder's article. The problem we look at is Leetcode 658. Find K Closest Elements: Let \(A\) be a sorted array of \(N\) values. We want to find the index \(j\) such that the elements \(A_j,A_{j+1},\dots,A_{j+k−1}\) have the \(k\) closest values to the given target value \(T\).
The generalization of binary search is done by formalizing how we reduce the search space by half: binary search can be used
if and only if for all \(x\) in the search space \(S\) (i.e., the ordered set), \(p(x)\) implies \(p(y)\) for all \(y > x\) (\(p\) stands
for some predicate over \(S\)). TopCoder article calls this formalization as main theorem. We use this theorem to discard
the second half of search space. For example, in the most basic binary search problem in the ascending order array, our predicate \(p\)
is defined as: is the value at i
smaller than X
? If answer is yes, we discard all the values
with index smaller than \(i\) because given the ascending order and A[i]
is smaller than X
, any value comes before A[i]
is also
smaller than X
. By the same reasoning, if the answer is no, we discard the second half. We make some observations here:
 As you may have noticed, predicate is exactly what we check in the ifstatement (e.g.
X > A[i]
).  If
X < A[i]
, then for any \(y > i\), we haveX < A[y]
, which exactly matches the main theorem and that's how we can discard the second half of the array (i.e., search space).
Now, let's consider our example. What does it mean a selected subrange is optimal (i.e., \(k\) closest values to \(T\))? That means that we can neither move the subrange to the right (\(A_{j+k}  T > A_j  T\)) nor move the subrange to the left (\(A_{j+k1}  T > A_{j1}  T\)). In details, since the subrange includes \(k\) closest values to \(T\) and by moving it to right, we exclude \(A_{j}\) and include \(A_{j+k}\). Since the selected subrange is optimal, we must have \(A_{j+k}  T > A_j  T\). Thus, we can formalize our predicate as: for a given index \(j\), does \(A_{j+k}  T > A_j  T\) hold? Another piece of information we need for binary search is the invariant. From the question description we can see that the key to this question is finding \(j\). Thus, our invariant is: the index of the first number that is among the k closest values for the given target \(T\) (i.e., \(j\)) is in \([\text{left}, \text{right}]\).
Now, we have to show that main theorem holds given our predicate formalization. Let's discuss this point in details:

When \(A_{j+k}  T > A_j  T\) is true:

If \(A\) is sorted in ascending order, then we have three possible cases:
 \(T < A_j < A_{j+k}\). In this case, we have \(A_{j+k}  T > A_j  T\). Let \(d\) be an integer with range \(0 < d < k\), then we have \(A_{j+k+d}  T > A_{j+k}  T > A_{j+d}  T > A_{j}  T\). In other words, for any index \(i > j\), our predicate holds (\(A_{i+k}  T > A_i  T\)). Thus, we can directly discard the second half of the array. Note that we still want to keep \(j\) because it might be the optimal \(j\) we are looking for.
 \(A_j < T < A_{j+k}\). In this case, we have \(A_{j+k}  T > T  A_j\). Then, we have \(A_{j+k+d}  T > A_{j+k}  T > T  A_j > T  A_{j+d}\). Then the predicate still holds for \(i > j\).
 \(A_j < A_{j+k} < T\). This is impossible given our predicate condition.

If \(A\) is sorted in descending order, we also have three possible cases:
 \(T > A_j > A_{j+k}\). In this case, we have \(T  A_{j+k} > T  A_j\), which leads to \(T  A_{j+k+d} > T  A_{j+k} > T  A_{j+d} > T  A_j\). Again, our predicate holds for any index \(i > j\) and we can discard the second half of the array.
 \(A_j > T > A_{j+k}\). In this case, we have \(T  A_{j+k} > A_j  T\). We have \(T  A_{j+k+d} > T  A_{j+k} > A_j  T > A_{j+d}  T\). Predicate holds.
 \(A_j > A_{j+k} > T\). Impossible.

Note
The reason we have \(0 \le d \le k\) is that in the extreme case, we have \(j = N  k\) (otherwise, we won't have \(k\) elements) and it is unnecessary to have \(d\) goes beyond \(k\).

When \(A_{j1}  T > A_{j+k1}  T\) is true:

If \(A\) is sorted in ascending order, then we have three possible cases:
 \(T < A_{j1} < A_{j+k1}\). Impossible.
 \(A_{j1} < T < A_{j+k1}\). In this case, we have \(T  A_{j1} > A_{j+k1}  T\). Then, let \(d\) be an integer with range between 0 and \(j1\). We have \(T  A_{j1d} > T  A_{j1} > A_{j+k1}  T > A_{j+k1d}  T\). Thus, for any index \(i <= j\), we have \(A_{i1}  T > A_{i+k1}  T\). This suggests that we can discard first half of the array.
 \(A_{j1} < A_{j+k1} < T\). We have \(T  A_{j1} > T  A_{j+k1}\), which implies \(T  A_{j1d} > T  A_{j1} > T  A_{j+k1d} > T  A_{j+k1}\), which again the predicate holds.

If \(A\) is sorted in descending order, then we have three possible cases:
 \(T > A_j > A_{j+k}\). Impossible.
 \(A_j > T > A_{j+k}\). In this case, we have \(A_{j1}  T > T  A_{j+k1}\), which implies that \(A_{j1d}  T > A_{j1}  T > T  A_{j+k1} > T  A_{j+k1d}\). predicate holds: for all index \(i <= j\), we have \(A_{i1}  T > A_{i+k1}  T\), which means we can discard first half of the array and move subrange to the right.
 \(A_j > A_{j+k} > T\). In this case, we have \(A_{j1}  T > A_{j+k1}  T\), which imples that \(A_{j1d}  T > A_{j1}  T > A_{j+k1d}  T > A_{j+k1}  T\). predicate holds.

Once we verify the predicate satisifies the main theorem, the only thing we left is to build the connection between the invariant and predicate, and make sure the invariant holds during the loop execution. Let's first list out the code:
class Solution {
public:
vector<int> findClosestElements(vector<int>& arr, int k, int x) {
int left = 0;
int right = arr.size()  k;
while (left < right)
{
int mid = left + (right  left) / 2;
if (fabs(x  arr[mid]) <= fabs(arr[mid+k]  x))
{
right = mid;
}
else if (fabs(x  arr[mid1]) > fabs(x  arr[mid+k1]))
{
left = mid + 1;
}
}
return vector<int>(arr.begin() + left, arr.begin() + left + k);
}
};
Note
Notice that in the code we actually use \(A_{j+k}  T \ge A_j  T\) instead of \(A_{j+k}  T > A_j  T\). The reason
is because whenever there is a tie, the smaller elements are always preferred. Consider [1,2,3,4,5]
with \(k = 4\) and \(
T = 3\). Then, both [1,2,3,4]
and [2,3,4,5]
are the closest \(k\) elements to the \(T\) and sum of the elements to \(T\) distance
are the same, which is a tie. In this case, we prefer [1,2,3,4]
. If we strictly follow the predicate, we end up with
[2,3,4,5]
. Switching \(A_{j+k}  T > A_j  T\) to \(A_{j+k}  T \ge A_j  T\) still maintains the invariant in the loop
because when \(A_{j+k}  T = A_j  T\), shifting the subrange to the right doesn't give any improvement and by set right
to mid, we still ensure that the optimal \(j\) falls inside \([\text{left}, \text{right}]\).
\(A_{j+k}  T > A_j  T\) means we cannot move the subrange to the right to obtain the optimal subrange. We also show that
under the condition, we can discard the second half of the array. mid
represents \(j\) in our condition and by not moving
subrange to right, we are saying that the optimal \(j\) has to be the left of mid
. This implies that we can safely move
set \(\text{right}\) to mid
and still maintains the invariant during the loop. On the other hand, \(A_{j1}  T > A_{j+k1}  T\)
means that we cannot move the subrange to the left to obtain the optimal subrange. We also show that the inequality allows us
to discard the first half of the array. Since for given \(j\) (mid
), we have \(A_{j1}  T > A_{j+k1}  T\). We cannot
move subrange (indicating by \(j\) or mid
) to the left; we have to move to right. Thus, we set
\(\text{left}\) to mid+1
to narrow down the search space while maintainng the invariant unchanged.
Note
The above code is theoretically correct but it has fundamentally implementation issue: mid
can be 0,
which will lead to index out of bound error in else if (fabs(x  arr[mid1]) > fabs(x  arr[mid+k1]))
.
C++ doesn't enforce index out of bound error (i.e., undefined behavior) and the above code can run
successfully for certain complier on certain platform (leetcode obvious can). However, issue will happen
if you directly translate the above logic to another language. A safe way to do is to replace the else if
statement with else if ((mid >0 && fabs(x  arr[mid1]) > fabs(x  arr[mid+k1]))  fabs(x  arr[mid]) > fabs(arr[mid+k]  x))
,
which you can see is redundant and can be optimized. This is exactly what we are going to do next.
One thing to note that while(left < right)
means we haven't found the optimal \(j\) yet, which implies that we have to
either move the subrange to left or move the subrange to right. This provides us the further opportunity to optimize the above
code:
class Solution {
public:
vector<int> findClosestElements(vector<int>& arr, int k, int x) {
int left = 0;
int right = arr.size()  k;
while (left < right)
{
int mid = left + (right  left) / 2;
if (fabs(x  arr[mid]) <= fabs(arr[mid+k]  x))
{
right = mid;
}
else
{
left = mid + 1;
}
}
return vector<int>(arr.begin() + left, arr.begin() + left + k);
}
};
In the first version, we check two conditions explicitly and do nothing if both conditions are not true. However,
as we state in the previous paragraph, since we are still in the while
loop, that means one of those two conditions will be true.
In other words, there is no such case that both conditions are false and we are still in the loop. Thus, we can get rid of
one of the conditions and use else
instead. Another way of thinking is that we do nothing if both conditions are failed
and thus this third donothing case can be combined with the second else if (fabs(x  arr[mid1]) > fabs(x  arr[mid+k1]))
condition to form a else
statement.
There is another optimization code proposal I find online, which I don't think it is correct:
class Solution {
public:
vector<int> findClosestElements(vector<int>& arr, int k, int x) {
int left = 0;
int right = arr.size()  k;
while (left < right)
{
int mid = left + (right  left) / 2;
if (x  arr[mid] <= arr[mid+k]  x)
{
right = mid;
}
else
{
left = mid + 1;
}
}
return vector<int>(arr.begin() + left, arr.begin() + left + k);
}
};
But this code return the wrong answer for the following case: arr = [5,4,3,2,1], x = 2, k = 4
. The above
solution gives [5,4,3,2]
, which is wrong because [4,3,2,1]
is the closest elements to 2
. To see this,
we can invoke the predicate: 5
is 3 units away from 2
but 1
is only 1 unit away from 2
(\(A_j  T > A_{j+k}  T\)),
which implies we can shift the subrange to the right. More straightforward way is to simply calculate the sum of distance of
each element: [5,4,3,2]
has sum 3+2+1 = 6
while [4,3,2,1]
has sum 2+1+1 = 4
.
Conclusion
We give one example showing the essence of the binary search: main theorem, which is a formalization of how we discard values.
Predicate helps us to find what to write in the if
statement and invariant helps us to make sure we find the correct value.
In this post, we go through a relative formal proof of the correctness of our predicate. One thing to note that, the proof
is in fact induction: we use \(d\) to show inequalities hold for any index \(i > j\). A nicer but equivalent way we can do is simply use
the induction and show \(p(j+1)\) holds given \(p(j)\) is correct (we actually do \(p(j+d)\) holds given \(p(j)\) is correct). Another point
we should point out that we can derive the invariant
from predicate: we try to find the index of the first number that is among the k closest values for the given target \(T\). This is
the exact same number that will first give "yes" response to our predicate.