15-122 Principles of Imperative Computation
Recitation 4 - Fri Jan 21

Reviewing operators
Multiplication algorithm

Reviewing C0 operators

When you left shift an int by 1 bit, what is this equivalent to mathematically?

Since each 1 bit moves up one position, it represents the next highest power of 2, so it makes sense that shifting left 1 bit multiplies an int by 2. But remember that this is modulo 232. For example, consider the value 122 represented in two's complement notation using 8 bits:

binary         decimal equivalent
01111010       122

As we left shift one bit at a time, you will see that int does change sign depending on what is in the leftmost bit:

binary         decimal equivalent
01111010       +122
11110100       -12  (Think: +122*2 = +244 which is equivalent to -12 using 8 bits)
11101000       -24
11010000       -48
10100000       -96
01000000       +64  (Think: -96*2 = -192 which is equivalent to +64 using 8 bits)
10000000       -128 
00000000       0

When you shift right on bit position, you are dividing by 2 (using integer division, no remainder). Remember that when you shift an int right, the sign bit is duplicated. For example, shifting the value +122 right one bit at a time yields:

binary         decimal equivalent
01111010       +122
00111101       +61
00011110       +30
00001111       +15
00000111       +7
00000011       +3
00000001       +1
00000000       0
00000000       0     (shifting right doesn't change anything anymore)
00000000       0

For negative numbers, remember that the sign bit duplicates when you shift right:

binary         decimal equivalent
10000110       -122
11000011       -61
11100001       -31   (not -30 as you might expect!)
11110000       -16   (not -15 as you might expect!)
11111000       -8
11111100       -4
11111110       -2
11111111       -1
11111111       -1    (shifting right doesn't change anything anymore)
11111111       -1

This shows that shifting right one bit is integer division by 2 as you probably have seen before as long as the value you're shifting is positive. For negative numbers, if you shift right and a 1 bit shifts off the right end, then the "rounding" is done toward -infinity, not toward 0.

An example using invariants: Multiplication Algorithm

Consider the problem of multiplying two integers x and y and returning the result. The way we can approach this is by thinking about expressing one of the integers as the sum of a sequence of powers of 2. For example,

x * 37 = x * (32 + 4 + 1) = x * 32 + x * 4 + x * 1 = x * 25 + x * 22 + x * 20

If we think about 37 in binary (shown with 8 bits here for simplicity):


we see that there is a 1 for each power of 2 we need to use in our computation. Starting with an overall result of 0, we see that the right most bit (representing 20) is 1, so we need to add x to our overall result. If we shift 37 to the right one position, the next bit to consider is again in the rightmost position but it now represents 21 so we should shift x left one position to take this into account so if the rightmost bit is 1 again, we can add the correct x value to the result (that is, 2 * the original x). We repeat this process until we've processed all the bits in 37.

Here is a function that performs this computation:

int mult(int x, int y)
//@requires x >= 0 && y >= 0;
//@ensures \result == x*y;
  int n = x; int k = y;
  int res = 0;
  while (n != 0)
    //@loop_invariant __________________
    { if ((k & 1) == 1) res = res + n;
      k = k >> 1;
      n = n << 1;
  return res;

One thing you should notice is that we don't alter the values of x and y. Instead, we create local variables with copies of these values so we can alter them without changing the initial parameters. This is useful so we can use the initial parameters in our annotations and know that they represent the original values passed to the function.

Let's trace this with the example above to see how it works, with x = 2 and y = 37. Recall that k & 1 determines the value of the last bit of k to determine if we add in y or not. Let's assuming we're dealing with 8 bits values for simplicity (the results generalize to 32 bits easily):

n      k      res
2      37     0
4      18     2
8      9      2
16     4      10
32     2      10
64     1      10
-128   0      74
0      0      74

Based on this trace, we conjecture that the loop invariant is:

x*y == k*n + res

Looking at the loop invariant, it says that our current result plus k * n is always equal to x * y. That is, if we stopped part way through the loop with some partial result, the remaining k and n multiplied together would complete the total product.

Let's show that the invariant holds for this loop.

   1. Before the loop condition is tested for the first time, 
      k = x and n = y and res = 0:
      x*y = k*n + res
      x*y = x*y + 0
      x*y = x*y
   2. Assume x*y = k*n+res at the start of an iteration. 
      We wish to show that x*y = k'*n' + res' at the end of that iteration
      (just before the loop condition is tested again).
      We consider two cases.
      a. k is even, so k & 1 = 0.
         Then k' = k>>1 = k/2 and n' = n*2 and res' = res.
         Hence k'*n' + res' 
             = (k/2)*n*2 + res 
             = k*n+res 
             = x*y.
      b. k is odd, so k & 1 = 1.
         Then k' = k>>1 = (k-1)/2 and n' = n*2 and res' = res + n.
         Hence k'*n' + res'
             = (k-1)/2*n*2 + res + n
             = (k-1)*n + res + n
             = k*n - n + res + n
             = k*n+res 
             = x*y
      (So the loop invariant holds at the end of this iteration,
       which means it also holds for the start of the next iteration
       since the loop condition does not change the values of any variables.)

Our reasoning show that the invariant holds for EVERY iteration, since we know it holds at the start of iteration #1. Step 2 above shows that if it holds for iteration #1, it must at the start of iteration #2. If it holds at the start of iteration #2, by step 2 again, it must hold at the start of iteration #3, etc.

When reasoning with loop invariants, we also need to show that the loop must terminate. That is, we could get into an infinite loop where the loop invariant is always true, but we'd never return a result from our computation. For this example, after at most 32 left shifts on n, n must be 0 causing the loop to terminate. This is because each left shift inserts a 0 into the right end of the integer and drops off the leftmost bit. After at most 32 shifts, the entire integer must be all 0's.

We're shown that the loop invariant is valid for the loop and that the loop will terminate.

Now, what do we know immediately after the loop terminates? We know the loop invariant is true (since the loop condition doesn't change any values in our function) and n == 0. Thus:

    (x*y == k*n + res) && n == 0   ===>    x*y = res

Since the only thing we do after the loop terminates is return the result, we have shown that we will always return x*y as indicated by the postcondition. So one of the reasons to have a strong loop invariant is to be able to show that the result of the computation after the loop terminates is the postcondition you're expecting from your function.

Exercise: Does this function work if x or y is negative?

written by Tom Cortina, 1/21/11