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Most of the data structures described in this chapter are folklore. They
can be found in implementations dating back over 30 years. For example,
implementations of stacks, queues, and deques, which generalize easily
to the
,
and
structures described
here, are discussed by Knuth [46, Section 2.2.2].
Brodnik et al. [13] seem to have been the first to describe
the
and prove a 
lower-bound like that
in Section 2.6.2. They also present a different structure
that uses a more sophisticated choice of block sizes in order to avoid
computing square roots in the
method. Within their scheme,
the block containing
is block
, which
is simply the index of the leading 1 bit in the binary representation
of
. Some computer architectures provide an instruction for
computing the index of the leading 1-bit in an integer.
A structure related to the
is the two-level
tiered-vector of Goodrich and Kloss [35].
This structure
supports the
and
operations in constant time and
and
in
time. These running times
are similar to what can be achieved with the more careful implementation
of a
discussed in Exercise 2.10.
method
inserts all elements of the
into the list at position
. (The
method is a special
case where
.) Explain why, for the data structures
in this chapter, it is not efficient to implement
by
repeated calls to
. Design and implement a more efficient
implementation.
.
This is an implementation
of the
interface in which the
operation removes
an element that is chosen uniformly at random among all the elements
currently in the queue. (Think of a
as a bag in which
we can add elements or reach in and blindly remove some random element.)
The
and
operations in a
should run
in constant time per operation.
(triple-ended queue).
This is a
implementation in which
and
run in constant time
and
and
run in time
that ``rotates'' the array
so that
![$ \mathtt{a[i]}$](img1404.png){kind=small}
moves to
![$ \ensuremath{\mathtt{a}}[(\ensuremath{\mathtt{i}}+\ensuremath{\mathtt{r}})\bmod \ensuremath{\mathtt{a.length}}]$](img1405.png){kind=small}
, for all
.
that ``rotates'' a
so that
list item
becomes list item
. When run on
an
, or a
,
should run in
time.
implementation so that the shifting
done by
,
, and
is done using
the faster
method.
implementation so that it does not use the
operator (which is expensive on some systems). Instead, it
should make use of the fact that, if
is a power of 2,
then
is the bitwise-and operator.)
that does not do any
modular arithmetic at all. Instead, all the data sits in a consecutive
block, in order, inside an array. When the data overruns the beginning
or the end of this array, a modified
operation is performed.
The amortized cost of all operations should be the same as in an
.
Hint: Getting this to work is really all about how you implement
the
operation. You would like
to put the data
structure into a state where the data cannot run off either end until
at least
operations have been performed.
Test the performance of your implementation against the
.
Optimize your implementation (by using
)
and see if you can get it to outperform the
implementation.
that has
only
wasted space, but that can perform
and
operations in
time.
that has
only
wasted space, but that can perform
and
operations in
time. (For an idea on how to do this, see Section 3.3.)
that has
only
wasted space, but that can perform
and
operations in
time.
(See Section 3.3 for ideas on how to achieve this.)
.
This three level structure
implements the
interface using
wasted space.
In this structure,
and
take constant time; while
and
take
amortized time.opendatastructures.org