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In this
project we describe a very general problem called Hiring problem that can be
used to express a wide variety of different problems. We can use different
algorithm for Hiring problem to solve the different problems like minimum
spanning tree, solving hiring problem etc. We will mainly discuss the setting,
difference between randomized algorithms and probabilistic analysis and how to
code up various problems as Hiring problem at the end, we will brie?y describe
some of the algorithms for solving Hiring problems.

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what is Hiring Problem

The problem
discusses the hiring of a candidate from candidates that an employment agency
sends you one each each day. We interview the person and than decide weather to
keep him or not. Each time we hire a candidate we have to fire the previous
one. Interviewing and hiring a candidate cost us some money. The main objective
here is to estimate that how much price does it costs you in order to hire the
best candidate for your office.

Algorithms to solve hiring

How can we
solve hiring problem? The standard algorithm for sloving Hiring problem is
Randomized algorithm. Historically, first randomized algorithm was a function
developed by a person named Michael O.Rabin. This study was further taken in
1977 by Robert M.Solovay and Volker Strassen, and they came up with a
randomized primality test. The after some year Micheal O.Rabin explained that
1977 randomized test can be turned into an algorithm, which after some time was
given a name of Randomized Algorithm. A randomized algorithm is an algorithm
that take randomness as an input or as a part of its operation. It often aims
for major properties like good average case behavior, outputting exact answer
with high probability and getting answers that are very much close to right


are the few advantages of Randomized Algorithms:

Simplicity: Most of randomized
algorithms are simpler than the best algorithms or in other words deterministic
algorithms for the same problem.

Spent time: For
repeated elements the same output by randomized algorithm is more better as
compared to the other deterministic algorithms.

 it performs best for a repeated number of
given time.

to practice.

also shown to yield improved Complexity bounds.


are the some limitations of above discussed algorithm:

Failure: Max
time with continuous progress can cause a massive hardware failure.

Runtime: As
these algorithms works in loops, so during a process running, it may split into
continuous parts and causing a longer run time to get final results.

Space Required: As it a algorithm that works continuously and again and again, it thus
require a lot of storage space on the main memory.


are the few applications of randomized algorithms:

The Birthday Paradox

How many people must there be in a room
before there is a 50% chance that two of them were born on the same day of the

The answer is surprisingly few. The paradox
is that it is in fact far fewer than the number of days in a year, or even half
the number of days in a year.

Balls And Bins.

Consider a process in which
we randomly toss identical balls into b bins,
numbered 1;2; : : : ;b. The tosses are
independent, and on each toss the ball is equally likely to end up in any bin.
The probability that a tossed ball lands in any given bin is 1=b. Thus, the ball-tossing
process is a sequence of Bernoulli trials with a probability 1=b of success, where success
means that the ball falls in the given bin. This model is particularly useful
for analyzing hashing, and we can answer a variety of interesting questions
about tossing ball process.


Suppose we flip a coin in air n number of
time. What is the highest possibility of consecutive heads that we can expect to
see? The answer can be given by using this algorithm.


The one line hiring problem

We do not wish to interview
all the candidates in order to find the best one. We also do not wish to hire
and fire as we find better and better applicants. In other words, we are
willing to select for a candidate who is close enough to the best, this in
exchange for hiring exactly once. We must obey one company requirement, so after
each interviewing each candidate we must either immediately offer the job to
the applicant or immediately reject the applicant. What is the trade-off
between minimizing the amount of interviewing and maximizing the quality of the
candidate hired?

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