  Let $n$ denote the number of elements currently in a data structure. An
in-place heap is stored in the first $n$ locations of an array, uses $O(1)$
extra space, and supports the operations: minimum, insert, and extract-min. We
introduce an in-place heap, for which minimum and insert take $O(1)$ worst-case
time, and extract-min takes $O(\lg{} n)$ worst-case time and involves at most
$\lg{} n + O(1)$ element comparisons. The achieved bounds are optimal to within
additive constant terms for the number of element comparisons. In particular,
these bounds for both insert and extract-min -and the time bound for insert-
surpass the corresponding lower bounds known for binary heaps, though our data
structure is similar. In a binary heap, when viewed as a nearly complete binary
tree, every node other than the root obeys the heap property, i.e. the element
at a node is not smaller than that at its parent. To surpass the lower bound
for extract-min, we reinforce a stronger property at the bottom levels of the
heap that the element at any right child is not smaller than that at its left
sibling. To surpass the lower bound for insert, we buffer insertions and allow
$O(\lg^2{} n)$ nodes to violate heap order in relation to their parents.
