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A computer has 8 bits of memory for floating point representation.

The first is assigned for the sign, the next four bits for the exponent and the last three for the mantissa.

The computer has no representation for $infty$ and 0 is represented like in IEEE754. Assume that the mantissa starts with $textbase^-1$ and that to the left of the mantissa there is an implied 1 that does not consume a place value.

1. What is the smallest positive number that can be represented?




2. What is the machine epsilon




3. How many numbers in base 10 can be represented?

1. In general the exponent is $2^(textbits)-2$ so in this case we have $2^4-2=14$ so the exponent range from -6 to 7 so the smallest positive number is $1.001*2^-6=2^-6+2^-9=0.017578125$




2. To find machine epsilon we take $textbase^-(p-1)$ where $p$ is the number of significant bits in the mantissa which is $2^-(3-1)=2^-2=0.25$

How should I approach 3, and are my solutions to 1 and 2 correct?

Due to the finite precision of the computer, numbers used in calculations must conform to the format imposed by the machine. So only real numbers with a finite number of digits can be represented. A normalized floating point system $mathbbF=F(beta,p,e_textmin,e_textmax)$ consists of a set of real numbers written in normalized floating point form $x=pm m times beta^e$, where $m$ is the mantissa of $x$ and $e$ is the exponent.

If $x neq 0$ then the mantissa $m$ can be written as:
beginequation
m = a_N +a_N-1 beta^-1+...+a_-p beta^-p-N
endequation
with $a_N neq 0$ and $e_textmin leq e leq e_textmax$. If $x=0$ then the mantissa $m=0$ while the exponent $e$ can take any value.

In the above expressions, $p$ is the precision of the system, $beta$ the base, and $[e_textmin,e_textmax]$ the exponent range, with $e_textmin<0$, and $e_textmax=|e_textmin|+1$.

According to the definition the mantissa $m$ belongs to the range $[1,beta)$. The machine epsilon is $beta^1-p$ and represents the difference between the
mantissae of two successive positive numbers.
Now a number $x$ belong to the range $[x_textmin, x_textmax]$ where:
beginequation
x_textmin = beta^e_textmin
endequation
and
beginequation
x_textmax = (beta-1)(1+beta^-1+beta^-2+... + beta^-(p-1)) beta^e_textmax< beta^e_textmax+1
endequation
We now prove the statement above. The general representation of $x in mathbbR$ in base $beta$ is:
beginequation
x=pm (a_N beta^N+a_N-1 beta^N-1+...+a_1 beta+a_0+a_-1 beta^-1+...+a_-p beta^-p)= pm m times beta^e
endequation
When we collect the terms $beta^N$ we have:
beginequation
x=pm (a_N +a_N-1 beta^-1+...+a_1 beta^-N+1+a_0 beta^-N+a_-1 beta^-1-N+...+a_-p beta^-p-N) times beta^N= pm m times beta^e
endequation
We can identify $N$ with $e$ ($N=e$). Then:
beginequation
m=sum_i=-p^N a_i beta^i-N
endequation
The minimum value of $m$ is reached when $a_0=1$ and $a_i=0$ with $1 leq i leq p-1$. In this case $m=1$ and $x_textmin = beta^e_textmin$.
The maximum value of $m$ is obtained when $a_i=beta-1$ for all $0 leq i leq p-1$.

The machine epsilon is defined as $epsilon_M=beta^1-p$. It is a measure of the precision of the system, since it is a maximum bound on the relative distance between two consecutive numbers. It also represents the difference between the mantissae of two successive positive numbers. In normalized floating point systems, no number that does not fit the finite format imposed by the computer can be represented.

The total number of elements in $mathbbF$ is given by the following expression:
beginequation
2 (beta-1) beta^p-1 (e_textmax-e_textmin+1)+2
endequation
Computers can work with single- or double-precision. IEEE standard single-precision floating point numbers belong to the normalized floating point system $F(2, 24, −126, +127)$, while IEEE standard double-precision floating point numbers belong to the normalized floating point system $F(2, 53, −1022, +1023)$.