Jingsen Chen

Efficient Algorithms for k Maximum Sums

We study the problem of computing the k maximum sum subsequences. Given a sequence of real numbers

Efficient algorithms for k maximum sums

\left\langle x_{1},x_{2},\ldots ,x_{n}\right\rangle

Space-efficient range-sum queries in OLAP

and an integer parameter k,

Heaps with bits

1\leq k\leq \frac{1}{2}n(n-1),

A Framework for Constructing Heap-Like Structures In-Place

the problem involves finding the k largest values of

Heaps with Bits

\sum_{\ell =i}^{j}x_{\ell }

Computing maximum-scoring segments in almost linear time

for

Heap Construction: Optimal in Both Worst and Average Cases?

1\leq i\leq j\leq n.

Average Cost to Produce Partial Orders

The problem for fixed k = 1, also known as the maximum sum subsequence problem, has received much attention in the literature and is linear-time solvable. Recently, Bae and Takaoka presented a

Some Lower Bounds for Comparison-Based Algorithms

\Theta(nk)