java代写: 考察算法复杂度

考察算法复杂度的概念,计算和证明。 使用java进行实验。

PA4: Runtime, Measured and Modeled

This assignment will give you experience working with big-Ο/θ/Ω
representations, practice matching them to implementations, and perform
measurements of the runtime of different methods.

This assignment is inspired by a combination of a lab in Swarthmore
College’s CS35, and by a similar assignment by Marina Langlois in CSE12

Deadlines and Milestones

You will submit a quiz and implementations of measure and
measurementsToCSV (described below) by Monday at 11:59pm, with the
rest due by Wednesday at 11:59pm.

We will provide implementations of measure and measurementsToCSV on
Tuesday; as a result no late points will be given for submissions of
that code submitted past Monday night, no exceptions.

There will be three submission entries in Gradescope, one for your
initial code (due Monday midnight), and two others for your final
submission: one for for your writeup (as a PDF) and one for your final
code. Make sure you know how to generate and submit a PDF early on in
the assignment process.

Read the whole writeup before starting – there are several different
pieces of the assignment you will need to hand in.

Big-O Justification

Justify your answers for questions 1, 3, 7, and 9 in the PA review

If you are justifying the positive direction, give choices of n0 and
C. For big-Θ, make sure to justify both big-O and big-Ω.


give a definition of big-Θ and big-Ω, which were not covered in detail
in class. The strategies we showed in class for big-O can also be used
for big-Ω, and big-Θ simply combines the two.

If you are justifying the negative direction, indicate which term(s)
can’t work because one is guaranteed to grow faster or slower than the

As a quick guide, here is an ordering of functions from slowest-growing
(indeed, the first two shrink as n increases) to fastest-growing that
you might find helpful:

  • f(n) = 1/(n2)
  • f(n) = 1/n
  • f(n) = 1
  • f(n) = log(n)
  • f(n) = sqrt(n)
  • f(n) = n
  • f(n) = n2
  • f(n) = n3
  • f(n) = n4
  • … and so on for constant polynomials …
  • f(n) = 2n
  • f(n) = n!
  • f(n) = nn

Provide this written up on the first page of pa4.pdf.

List Analysis

Consider the two files
which are the starter files from PA2. Answer the following questions,
and justify them with one or two sentences each:

  • Give a tight big-O bound for the best case running time of
    prepend in CSE12ArrayList
  • Give a tight big-O bound for the best case running time of
    prepend in CSE12DLList
  • Give a tight big-O bound for the worst case running time of
    prepend in CSE12ArrayList
  • Give a tight big-O bound for the worst case running time of
    prepend in CSE12DLList
  • Give a tight big-O bound for the best case running time of
    append in CSE12ArrayList
  • Give a tight big-O bound for the best case running time of
    append in CSE12DLList
  • Give a tight big-O bound for the worst case running time of
    append in CSE12ArrayList
  • Give a tight big-O bound for the worst case running time of
    append in CSE12DLList

In all cases, give answers in terms of the current size of the list.

Notable points to consider:

  • Creating an array takes time proportional to the length of the array
  • When considering the running time of a method, make sure to take
    into account any helpers methods it uses!

Example for getAt in the CSE12DLList class:

The getAt method is O(1) in the best case, when the index is 0. In this
case it will do constant work checking the index and immediately return the
first element, never entering the while loop.

The getAt method is O(n) in the worst case, because the index could be at
the end of the list (for example, index n - 1). In this case, the while
loop will run n times, spending constant time on each iteration, resulting
in overall O(n) number of steps taken.

Provide this written up on the second and third pages of pa4.pdf.

Mystery Functions

We have provided you with a .jar file that contains implementations of
the following methods:

public static void f1(int n) {
int a = 0;
for (int i = 0; i < n; i += 1) {
a = i;
public static void f2(int n) {
int a = 0;
for(int i = 0; i < n; i += 2) {
a = i;
public static void f3(int n) {
int a = 0;
for(int i = 0; i < n * n; i += 1) {
a = i;
public static void f4(int n) {
int a = 0;
for(int i = 0; i < n; i += 1) {
for(int j = i; j < n; j += 1) {
a = i + j;
public static void f5(int n) {
int a = 0;
for(int i = 0; i < n * n; i += 1) {
for(int j = 0; j <= i / 2; j += 1) {
a = i + j;
public static void f6(int n) {
int k = 1, a = 0;
for(int i = 0; i < n; i += 1) {
for(int j = 0; j <= k * 2; j += 1) {
a = i + j;
k = k * 2;

However, in that file, they are called mysteryA-F, and they are in a
different order, and we don’t provide the source of that file. You have
two tasks: determining a tight big-O bound for each method labeled 1-6
analyzing the source above, and determining which mystery method A-F
corresponds to the implementations above by measuring against provided
(but hidden) implementation.

Identifying Bounds from Code

Determine a tight big-O bound for each function, and justify it with a
few sentences. Give only the most relevant term, so use, for example
O(n), not O(4n + 2) Provide this written up on the fourth page of

Measuring Implementations

You will write a program to:

  • Measure the mystery methods
  • Use your measurements to match the mystery methods to the sources
  • Generate several graphs to justify your work

You have a lot of freedom in how you do this; the deliverables you need
to produce are specified at the end of this section. There are a few
methods that we require that you write in order to do this, and they
will help guide you through the measurement process.

The measure Method

You must write the following two methods in the Measure class:

public static List<Measurement> measure(String[] toRun, int startN, int stopN)`
public static String measurementsToCSV(List<Measurement> measurements)

where Measurement is defined in

  • measure should work as follows:

    1. It assumes each string in toRun is one of the letters A-F.

    2. For each of the implementations to run, it runs the
      corresponding mysteryX method stopN - startN times,
      providing a value of n starting at startN and ending at
      stopN each time.

    3. For each of these runs, it measures the time it takes to run.
      You can do this by using the method System.nanoTime() (see an
      example in lecture

    4. For each of the measured runs, it creates a Measurement whose
      valueOfN field is the value that was used for the given run,
      whose name field is the single-letter string of the
      implementation that ran, and whose nanosecondsToRun field is a
      measurement, and adds it to a running list of measurements.

    5. The final result is the list of measurements.


This call:

measure(new String[]{"A", "B"}, 40, 100);

Should produce a list that has 122 measurements, 61 of which will have
name equal to "A" and 61 of which will have name equal to "B".
Each of the 61 for each name will have a different valueOfN from 40 to
100, and each will have a different number of nanoseconds (as was

The measurementsToCSV method

The measurementsToCSV method takes a list of measurements (for
example, as returned from measure) and generates a
comma-separated-values String of the measurements. It should have the
following format, where the first row is a literal header row and the
other rows are example data. Note that this data is completely made up,
and may not match your measurements.

You might choose to put all of the measurements for a single letter

... many rows for A ...
... many rows for B ...

You might also choose to put all of the measurements for a single round
of n together:

... many alternating rows of A, B ...

Either layout is fine, do what makes sense to you, or what matches your
measure function best, etc.

Strategies for Measuring

You can use the measure and measurementsToCSV methods to produce
data about how the functions behaved in terms of their runtime. You
should fill in the main method with whatever you find useful for using
your measuring methods to compare the mystery implementations. You have
total choice in how you implement this – you can add new helpers, print
the CSV format out to a file, copy/paste it into a spreadsheet, use a
tool you like for plotting, etc. The goal is to use measurements to
identify the different implementations. Feel free to look up
documentation for writing Strings out to files and use it, or use
System.out.println and copy/paste the output, etc. It’s probably
pretty expedient to copy the data into Excel or a Google Sheet.

There are a few high-level strategies to consider:

  • If an implementation is very slow, it could take a really long time
    to measure it for large n. If you notice something is taking a long
    time, stop the program and try the same mystery methods on a smaller
    input range. Does the smaller range tell you anything useful?
  • Some of the methods might have similar big-O bounds, but have
    different constants that can be measured in terms of absolute time.
  • Some of the methods might take vastly different times to run on
    certain inputs, so plotting them next to one another will show one
    with a flat line at 0 and the other with some interesting curve.
    Make sure to check what the relative numbers are when inspecting the

You will use these measurements to figure out which mystery method
matches the implementations above, and generate three graphs to justify
your answers.

Avoiding Obscuring Optimizations

On many platforms and Java versions, simple methods like the above get
optimized to run much faster than their theoretical number of steps
might suggest. Java is pretty smart – it can, while running, figure out
how to make them run quickly enough that empirical measurements become
hard to make. If you’re seeing that even on values of n in the hundreds
of thousands, you get effectively constant behavior, you should try
disabling these optimizations to get more useful measurements for
distinguishing the implementations.

Instructions for doing this are in the Turning Off Java Optimizations
section of this Google Doc (scroll to the end):

Note that this will make all the mystery methods run a lot slower, so
you may want to decrease the values of n you use after making this
change to avoid waiting a long time.

Deliverables for Measurements

You will hand in:

  • Your code with the implementations above, by Monday 11:59pm, by the
    usual Gradescope handin, in the assignment pa4.
  • All of your code, in the assignment pa4-final-code, by the usual
    gradescope handin by Wednesday 11:59pm.
  • On the fourth page and beyond in your pa4.pdf:
    • The BigO bounds for each implementation f1-6.
    • A listing that matches each of mysteryA-F to an implementation
      f1-6 above
    • Three graphs that justify a few choices above. These don’t need
      to exhaustively describe all of your matchings, but they must be
      generated from real data that you measured using measure, and
      they must show an interesting relationship that helps justify
      the matching.

If you want a guide on how to get from the CSV data to a graph, look

Submission Instructions

There are four artifacts to submit for this PA:

By Monday, 11:59pm

  • The review quiz linked above
  • The pa4 assignment in Gradescope, where you will submit your code
    as usual from Github.

By Wednesday, 11:59pm

  • The pa4-final-code assignment in Gradescope, where you will submit
    your final code for performing measurements, along with a README
    describing how you measured things. This README should just contain
    a few sentences describing how you ran your program to generate
  • The pa4-written assignment in Gradescope, where you will submit a
    single PDF file called pa4.pdf.
    • The first page should have your big-O justifications, which
      should take up one page (you don’t have to write a page of text!
      But don’t put any other answers on the first page)
    • The second and third pages should have your List analysis,
      which should take up pages 2 and 3 (you don’t have to write two
      pages of text! But don’t put any other answers on the second or
      third pages)
    • The rest of the pages should have your matchings for the
      mystery functions, along with your graphs and justifications

Grade Breakdown

  • 15% startup work
    • 3% writeup quiz
    • 4% measurementsToCSV
    • 8% measure
  • 20% big-O justifications
  • 20% list big-O analysis
  • 5% final code and README – we will just check that you did
    something resonable here, to help show where the data for the
    matching came from
  • 40% matching activity
    • 24% big-O descriptions of f1-6
    • 6% correct matching
    • 10% three relevant graphs
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