# Introduction the loop and due to repeatation of

Introduction

In
this term paper will explain a most common problem called Hiring Problem.
Hiring Problem have a huge variety of different problems that can be solved by
using Hiring Problem algorithm. By using different algorithm for hiring tree to
solve many problems like secretary problem or marriage problem. In this term
paper we will discuss the randomized algorithm and probabilistic analysis of a
problem by using this will also discuss other problem as hiring problem. We
will explain some algorithm to solve the hiring problems.

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How to define Hiring Problem

Hiring
problem is such a problem in which a company hire a best candidate with the
help of employment agency, the agency sends you a one candidate each day. And
our duty is that to interview the candidate and decide the candidate is
suitable for the company or not. And at each interview we need to hire a best
candidate from the available list to fire the fire previous one. Our main
Purpose is that to calculate or estimate the hiring cost of a best candidate
towards the employment agency.

Algorithms for Hiring Problem

There
is different algorithm to solve the Hiring Problem. But the most common and
standard algorithm to solve the hiring problem is randomized algorithm. A
randomized algorithm is one that receives, in addition to its input data, a
stream of random bits that it can use for making random choices. By using this
we can analysis the best , average and worst case of hiring through which will
hire a such a candidate who have higher best probability according to interview
standards.

There
are some advantages toward the Randomized algoritm.

Efficiency:

For
the repeated element identification and primary testing grabs the most effective
part with countless results.

Simplicity:

A
majority of the randomized algorithm found in the literature are simpler than
best deterministic algorithm for the same problem.

Complexity:

This algorithm shows the better complexity bounds. This
algorithm is also easy to practice.

Drawbacks

Time:

This type of
algorithm works in the loop and due to repeatation of the several process it
take the many time slots.

Hardware:

By continuous running
this type of algorithm we face hardware failure problem.

Space:

As the algorithm
works the different process as many time again and again then it requires a
large storage space.

PacMan Game is the
best example of this algorithm and also face these limitations of algorithms.

Applications

These are the few
applications of randomized algorithm.

Our ?rst
example is 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 year?
The answer is surprisingly few. According to a
analysis a random group of just 23 people there is about a 50–50 chance that
two of them will have the same birthday. This is known as the birthday paradox.

Balls AND PINS:

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
the ball-tossing process.

Streaks:

Suppose you ?ip a fair coin n times. What is the longest streak of
consecutive heads that you expect to see? The answer is ‚Theta( lg n ), and it
can be shown by this algorithm.