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It's add modulo 2^512. Could you explain me why we doing here >>8 and then &oxFF?
I know i'm bad in math.
int AddModulo512(int []a, int []b)
{
int i = 0, t = 0;
int [] result = new int [a.length];
for(i = 63; i >= 0; i--)
{
t = (a[i]) + (int) (b[i]) + (t >> 8);
result[i] = (t & 0xFF); //?
}
return result;
}
The mathematical effect of a bitwise shift right (>>) on an integer is to divide by two (truncating any remainder). By shifting right 8 times, you divide by 2^8, or 256.
The bitwise & with 0xFF means that the result will be limited to the first byte, or a range of 0-255.
Not sure why it references modulo 512 when it actually divides by 256.
It looks like you have 64 ints in each array, but your math is modulo 2^512. 512 divided by 64 is 8, so you are only using the least significant 8 bits in each int.
Here, t is used to store an intermediate result that may be more than 8 bits long.
In the first loop, t is 0, so it doesn't figure in the addition in the first statement. There's nothing to carry yet. But the addition may result in a value that needs more than 8 bits to store. So, the second line masks out the least significant 8 bits to store in the current result array. The result is left intact to the next loop.
What does the previous value of t do in the next iteration? It functions as a carry in the addition. Bit-shifting it to the right 8 positions makes any bits beyond 8 in the previous loop's result into a carry into the current position.
Example, with just 2-element arrays, to illustrate the carrying:
[1, 255] + [1, 255]
First loop:
t = 255 + 255 + (0) = 510; // 1 11111110
result[i] = 510 & 0xFF = 254; // 11111110
The & 0xFF here takes only the least significant 8 bits. In the analogy with normal math, 9 + 9 = 18, but in an addition problem with many digits, we say "8 carry the 1". The bitmask here performs the same function as extracting the "8" out of 18.
Second loop:
// 1 11111110 >> 8 yields 0 00000001
t = 1 + 1 + (510 >> 8) = 1 + 1 + 1 = 3; // The 1 from above is carried here.
result[i] = 3 & 0xFF = 3;
The >> 8 extracts the possible carry amount. In the analogy with normal math, 9 + 9 = 18, but in an addition problem with many digits, we say "8 carry the 1". The bit shift here performs the same function as extracting the "1" out of 18.
The result is [3, 254].
Notice how any carry leftover from the last iteration (i == 0) is ignored. This implements the modulo 2^512. Any carryover from the last iteration represents 2^512 and is ignored.
>> is a bitwise shift.
The signed left shift operator "<<" shifts a bit pattern to the left,
and the signed right shift operator ">>" shifts a bit pattern to the
right. The bit pattern is given by the left-hand operand, and the
number of positions to shift by the right-hand operand. The unsigned
right shift operator ">>>" shifts a zero into the leftmost position,
while the leftmost position after ">>" depends on sign extension.
& is a bitwise and
The bitwise & operator performs a bitwise AND operation.
https://docs.oracle.com/javase/tutorial/java/nutsandbolts/op3.html
http://www.tutorialspoint.com/java/java_bitwise_operators_examples.htm
>> is the bitshift operator
0xFF is the hexadecimal literal for 255.
I think your question misses a very important part, the data format, i.e. how data are stored in a[] and b[]. To solve this question, I make some assumptions:
Since it's modulo arithmetic, a, b <= 2^512. Thus, a and b have 512 bits.
Since a and b have 64 elements, only 8 right-most bits of each elements are used. In other words, a[i], b[i] <= 256.
Then, what remains is very straightforward. Just consider each a[i] and b[i] as a digit (each digit is 8-bit) in a base 2^512 addition and then perform addition by adding digit-by-digit from right-to-left.
t is the carry variable which stores the value (with carry) of the addition at the last digit. t>>8 throws a way the right-most 8 bits that has been used for the last addition which is used as carry for the current addition. (t & 0xFF) gets the right-most 8 bits of t which is used for the current digit.
Since it's modulo addition, the final carry is thrown away.
I am writing a program which I found on a coding competition website, I have sort of figured out how to solve the problem but, I am stuck on a math part of it, I am completely diluting the problem and showing what I need.
first I need to check if a number is part of a sequence, my sequence is 2*a+1 where a is the previous element in the sequence or 2^n-1 to get nth item in the sequence. so it is 1,3,7,15,31,63...
I don't really want to create the whole sequence and check if a number is present, but I am not sure what a quicker method to do this would be.
Second if I am given a number lets say 25, I want to figure out the next highest number in my sequence to this number. So for 25 it would be 31 and for 47 it would be 63, for 8 it would be 13.
How can i do these things without creating the whole sequence.
I have seen similar questions here with different sequences but I am still not sure how to solve this
Start by finding the explicit formula for any term in your sequence. I'm too lazy to write out a proof, so just add 1 to each term in your sequence:
1 + 1 = 2
3 + 1 = 4
7 + 1 = 8
15 + 1 = 16
31 + 1 = 32
63 + 1 = 64
...
You can clearly see that a_n = 2^n - 1.
To check if a particular number is in your sequence, assume that it is:
x = 2^n - 1
x + 1 = 2^n
From Wikipedia:
The binary representation of integers makes it possible to apply a
very fast test to determine whether a given positive integer x is a
power of two:
positive x is a power of two ⇔ (x & (x − 1)) equals to zero.
So to check, just do:
bool in_sequence(int n) {
return ((n + 1) & n) == 0;
}
As #Blender already pointed out your sequence is essentially 2^n - 1, you can use this trick if you use integer format to store it:
boolean inSequence(int value) {
for (int i = 0x7FFF; i != 0; i >>>= 1) {
if (value == i) {
return true;
}
}
return false;
}
Note that for every elements in your sequence, its binary representation will be lots of 0s and then lots of 1s.
For example, 7 in binary is 0000000000000000000000000000111 and 63 in binary is 0000000000000000000000000111111.
This solution starts from 01111111111111111111111111111111 and use an unsigned bitshift, then compare if it is equal to your value.
Nice and simple.
How to find the next higher number :
For example, we get 19 ( 10011 ) , should return 31 (11111)
int findNext(int n){
if(n == 0) return 1;
int ret = 2; // start from 10
while( (n>>1) > 0){ // end with 100000
ret<<1;
}
return ret-1;
}
I somehow have to keep my program running until the output of the exponent function exceeds the input value, and then compare that to the previous output of the exponent function. How would I do something like that, even if in just pseudocode?
Find logarithm to base 2 from given number => x := log (2, input)
Round the value acquired in step 1 both up and down => y := round(x), z := round(x) + 1
Find 2^y, 2^z, compare them both with input and choose the one that suits better
Depending on which language you're using, you can do this easily using bitwise operations. You want either the value with a single 1 bit set greater than the highest one bit set in the input value, or the value with the highest one bit set in the input value.
If you do set all of the bits below the highest set bit to 1, then add one you end up with the next greater power of two. You can right shift this to get the next lower power of two and choose the closer of the two.
unsigned closest_power_of_two(unsigned value)
{
unsigned above = (value - 1); // handle case where input is a power of two
above |= above >> 1; // set all of the bits below the highest bit
above |= above >> 2;
above |= above >> 4;
above |= above >> 8;
above |= above >> 16;
++above; // add one, carrying all the way through
// leaving only one bit set.
unsigned below = above >> 1; // find the next lower power of two.
return (above - value) < (value - below) ? above : below;
}
See Bit Twiddling Hacks for other similar tricks.
Apart from the looping there's also one solution that may be faster depending on how the compiler maps the nlz instruction:
public int nextPowerOfTwo(int val) {
return 1 << (32 - Integer.numberOfLeadingZeros(val - 1));
}
No explicit looping and certainly more efficient than the solutions using Math.pow. Hard to say more without looking what code the compiler generates for numberOfLeadingZeros.
With that we can then easily get the lower power of 2 and then compare which one is nearer - the last part has to be done for each solution it seems to me.
set x to 1.
while x < target, set x = 2 * x
then just return x or x / 2, whichever is closer to the target.
public static int neareastPower2(int in) {
if (in <= 1) {
return 1;
}
int result = 2;
while (in > 3) {
in = in >> 1;
result = result << 1;
}
if (in == 3) {
return result << 1;
} else {
return result;
}
}
I will use 5 as input for an easy example instead of 50.
Convert the input to bits/bytes, in this case 101
Since you are looking for powers of two, your answer will all be of the form 10000...00 (a one with a certain amount of zeros). You take the input value (3 bits) and calculate the integer value of 100 (3 bits) and 1000 (4 bits). The integer 100 will be smaller then the input, the integer 1000 will be larger.
You calculate the difference between the input and the two possible values and use the smallest one. In this case 100 = 4 (difference of 1) while 1000 = 8 (difference of 3), so the searched answer is 4
public static int neareastPower2(int in) {
return (int) Math.pow(2, Math.round(Math.log(in) / Math.log(2)));
}
Here's the pseudo code for a function that takes the input number and returns your answer.
int findit( int x) {
int a = int(log(x)/log(2));
if(x >= 2^a + 2^(a-1))
return 2^(a+1)
else
return 2^a
}
Here's a bitwise solution--it will return the lessor of 2^N and 2^(N+1) in case of a tie. This should be very fast compare to invoking the log() function
let mask = (~0 >> 1) + 1
while ( mask > value )
mask >> 1
return ( mask & value == 0 ) ? mask : mask << 1
I'm in my first programming course and I'm quite stuck right now. Basically, what we are doing is we take 16 values from a text file (on the first line of code) and there is a single value on the second line of code. We read those 16 values into an array, and we set that 2nd line value as our target. I had no problem with that part.
But, where I'm having trouble is creating a bitmap to test every possible subset of the 16 values, that equal the target number.
IE, say we had these numbers:
12 15 20 4 3 10 17 12 24 21 19 33 27 11 25 32
We then correspond each value to a bitmap
0 1 1 0 0 0 0 1 1 1 0 1 0 0 1 0
Then we only accept the values predicated with "1"
15 20 12 24 21 33 25
Then we test that subset to see if it equals the "target" number.
We are only allowed to use one array in the problem, and we aren't allowed to use the math class (haven't gotten to it yet).
I understand the concept, and I know that I need to implement shifting operators and the logical & sign, but I'm truly at a loss. I'm very frustrated, and I just was wondering if anybody could give me any tips.
To generate all possible bit patterns inside an int and thus all possible subsets defined by that bit map would simply require you to start your int at 1 and keep incrementing it to the highest possible value an unsigned short int can hold (all 1s). At the end of each inner loop, compare the sum to the target. If it matches, you got a solution subset - print it out. If not, try the next subset.
Can someone help to explain how to go about doing this? I understand the concept but lack the knowledge of how to implement it.
OK, so you are allowed one array. Presumably, that array holds the first set of data.
So your approach needs to not have any additional arrays.
The bit-vector is simply a mental model construct in this case. The idea is this: if you try every possible combination (note, NOT permutation), then you are going to find the closest sum to your target. So lets say you have N numbers. That means you have 2^N possible combinations.
The bit-vector approach is to number each combination with 0 to 2^N - 1, and try each one.
Assuming you have less that 32 numbers in the array, you essentially have an outer loop like this:
int numberOfCombinations = (1 << numbers.length - 1) - 1;
for (int i = 0; i < numberOfCombinations; ++i) { ... }
for each value of i, you need to go over each number in numbers, deciding to add or skip based on shifts and bitmasks of i.
So the task is to what an algorithm that, given a set A of non-negative numbers and a goal value k, determines whether there is a subset of A such that the sum of its elements is k.
I'd approach this using induction over A, keeping track of which numbers <= k are sums of a subset of the set of elements processed so far. That is:
boolean[] reachable = new boolean[k+1];
reachable[0] = true;
for (int a : A) {
// compute the new reachable
// hint: what's the relationship between subsets of S and S \/ {a} ?
}
return reachable[k];
A bitmap is, mathematically speaking, a function mapping a range of numbers onto {0, 1}. A boolean[] maps array indices to booleans. So one could call a boolean[] a bitmap.
One disadvanatage of using a boolean[] is that you must process each array element individually. Instead, one could use that a long holds 64 bits, and use bitshifting and masking operations to process 64 "array" elements at a time. But that sort of microoptimization is error-prone and rather involved, so not commonly done in code that should be reliable and maintainable.
I think you need something like this:
public boolean equalsTarget( int bitmap, int [] numbers, int target ) {
int sum = 0; // this is the variable we're storing the running sum of our numbers
int mask = 1; // this is the bitmask that we're using to query the bitmap
for( int i = 0; i < numbers.length; i++ ) { // for each number in our array
if( bitmap & mask > 0 ) { // test if the ith bit is 1
sum += numbers[ i ]; // and add the ith number to the sum if it is
}
mask <<= 1; // shift the mask bit left by 1
}
return sum == target; //if the sum equals the target, this bitmap is a match
}
The rest of your code is fairly simple, you just feed every possible value of your bitmap (1..65535) into this method and act on the result.
P.s.: Please make sure that you fully understand the solution and not just copy it, otherwise you're just cheating yourself. :)
P.p.s: Using int works in this case, as int is 32 bit wide and we only need 16. Be careful with bitwise operations though if you need all the bits, as all primitive integer types (byte, short, int, long) are signed in Java.
There are a couple steps in solving this. First you need to enumerate all the possible bit maps. As others have pointed out you can do this easily by incrementing an integer from 0 to 2^n - 1.
Once you have that, you can iterate over all the possible bit maps you just need a way to take that bit map and "apply" it to an array to generate the sum of the elements at all indexes represented by the map. The following method is an example of how to do that:
private static int bitmapSum(int[] input, int bitmap) {
// a variable for holding the running total
int sum = 0;
// iterate over each element in our array
// adding only the values specified by the bitmap
for (int i = 0; i < input.length; i++) {
int mask = 1 << i;
if ((bitmap & mask) != 0) {
// If the index is part of the bitmap, add it to the total;
sum += input[i];
}
}
return sum;
}
This function will take an integer array and a bit map (represented as an integer) and return the sum of all the elements in the array whose index are present in the mask.
The key to this function is the ability to determine if a given index is in fact in the bit map. That is accomplished by first creating a bit mask for the desired index and then applying that mask to the bit map to test if that value is set.
Basically we want to build an integer where only one bit is set and all the others are zero. We can then bitwise AND that mask with the bit map and test if a particular position is set by comparing the result to 0.
Lets say we have an 8-bit map like the following:
map: 1 0 0 1 1 1 0 1
---------------
indexes: 7 6 5 4 3 2 1 0
To test the value for index 4 we would need a bit mask that looks like the following:
mask: 0 0 0 1 0 0 0 0
---------------
indexes: 7 6 5 4 3 2 1 0
To build the mask we simply start with 1 and shift it by N:
1: 0 0 0 0 0 0 0 1
shift by 1: 0 0 0 0 0 0 1 0
shift by 2: 0 0 0 0 0 1 0 0
shift by 3: 0 0 0 0 1 0 0 0
shift by 4: 0 0 0 1 0 0 0 0
Once we have this we can apply the mask to the map and see if the value is set:
map: 1 0 0 1 1 1 0 1
mask: 0 0 0 1 0 0 0 0
---------------
result of AND: 0 0 0 1 0 0 0 0
Since the result is != 0 we can tell that index 4 is included in the map.
I am unable to understand the following:
In java,
long l = 130L;
byte b = (byte)l;
If I print the value of b, why do I get -126? What is the bit representation of long l?
A byte is a sequence of 8 bits, which makes 2^8 cases = 256. Half of them represent negative numbers, which is -128 to -1. Then there is the 0, and about the half, 1 to 127 represent the positive numbers.
130 as Int looks like 128 + 2 which is:
0000:0000 1000:0000 (128)
0000:0000 0000:0010 (2)
0000:0000 1000:0010 (130)
However, the Byte has just 8 digits, and the assignment takes just the bits as they are, but just the last ones:
1000:0010
The first bit indicates, it is a negative number. Now how much do you need to add to get to zero? Let's do it stepwise:
1000:0010 x +
0000:0001 1 =
----------------
1000:0011 (x+1)
1000:0011 (x+1) +
0000:0001 1 =
----------------
1000:0100 (x+2)
Lets do bigger steps. Just add 1s where we have zeros, but first we go back to x:
1000:0010 x +
0111:1101 y =
--------------
1111:1111
Now there is the turning point: we add another 1, and get zero (plus overflow)
1111:1111 (x + y) +
0000:0001 1
---------
0000:0000 0
If (x+y) + 1 = 0, x+y = -1. A minus 1 is, interestingly, not just the same as 1 (0000:0001) with a 'negative-flag' set ('1000:0001'), but looks completely different. However, the first position always tells you the sign: 1 always indicates negative.
But what did we add before?
0111:1101 y = ?
It doesn't have a 1 at the first position, so it is a positive value. We know how to deconstruct that?
..f:8421 Position of value (1, 2, 4, 8, 16=f, 32, 64 in opposite direction)
0111:1101 y = ?
..f 84 1 = 1+4+8+16+32+64= 125
And now it's clear: x+125 = -1 => x = -126
You may imagine the values, organized in a circle, with the 0 at the top (high noon) and positive values arranged like on a clock from 0 to 5 (but to 127), and the turning point at the bottom (127 + 1 => -128 [sic!].) Now you can go on clockwise, adding 1 leads to -127, -126, -125, ... -3, -2, -1 (at 11 o'clock) and finally 0 at the top again.
For bigger numbers (small, int, long) take bigger clocks, with the zero always on top, the maximum and minimum always on bottom. But even a byte is much too big, to make a picture, so I made one of a nibble, a half-byte:
You can easily fill the holes in the picture, it's trivial!
Btw.: the whole thing isn't called casting. Casting is only used between Objects. If you have something, which is in real a subtype:
Object o = new String ("casting or not?");
this is just an assignment, since a String is (always) an Object. No casting involved.
String s = (String) o;
This is a casting. To the more specific type. Not every object is a String. There is a small relationship to integer promotion, since every byte can be lossless transformed to long, but not every long to byte. However, even Byte and Long, the Object-types, aren't inherited from each other.
You just don't get a warning, for
byte s = (byte) 42;
long o = s; // no problem, no warning
byte b = (byte) o; // written like casting
Bytes are signed in Java - so the range of values is -128 to 127 inclusive.
The bit pattern for 130 as a long, when simply truncated to 8 bits, is the bit pattern for -126 as a byte.
As another example:
int x = 255;
byte b = (byte) x; // b is now -1
You mean byte b = (byte)l?
Java's types are signed, so bytes allow numbers between -128 and +127.
For beginners to understand:
1 byte = 8 bits
Range (derived from 2's complement no. system) = [-2^(n-1) to 2^(n-1)-1], where n is no. of bits
So range is -128 to 127
Whenever value is incremented more than highest possible +ve value, the flow goes to the lowest possible -ve value.
So after value reaches 127 , flow continues from -128 to -127 to -126
to cover a total space of 130 and the thus o/p is -126