multiplication

I have been trying to achieve something which should pretty trivial and is trivial in Matlab . I want to simply achieve something such as: cv::Mat sample = [4 5 6; 4 2 5; 1 4 2]; sample = 5*sample; After which sample should just be: [20 24 30; 20 10 25; 5 20 10] I have tried scaleAdd , Mul , Multiply and neither allow a scalar multiplier and require a matrix of the same "size and type". In this scenario I could create a Matrix of Ones and then use the scale parameter but that seems so very extraneous Any direct simple method would be great! OpenCV does in fact support multiplication by a

I was wondering how I could go about multiplying a series of binary bits using bitwise operators. However, I'm interested in doing this to find the a decimal fraction value for the binary value. Here's an example of what I'm trying to do: Given, say: 1010010, I want to use each individual bit so that it will be computed as: 1*(2^-1) + 0*(2^-2) + 1*(2^-3) + 0*(2^-4)..... Though I'm interested in doing this in ARM assembly, having an example in C/C++ will still help as well. I was thinking of performing a loop with a counter, where each time the loop iterates, a counter will increment, the value

(int)(33.46639 * 1000000) returns 33466389 Why does this happen? Floating point math isn't perfect. What every programmer should know about it. Floating-point arithmetic is considered an esoteric subject by many people. This is rather surprising because floating-point is ubiquitous in computer systems. Almost every language has a floating-point datatype; computers from PCs to supercomputers have floating-point accelerators; most compilers will be called upon to compile floating-point algorithms from time to time; and virtually every operating system must respond to floating-point exceptions

I need to multiply several 1000s digits long integers as efficiently as possible in Python. The numbers are read from a file. I am trying to implement the Schönhage-Strassen algorithm for integer multiplication, but I am stuck on understanding the definition and mathematics behind it, specially the Fast Fourier Transform. Any help to understand this algorithm, like a practical example or some pseudo-code would be highly appreciated. Chapter 4.3.3 of Knuth's TAOCP describes it and also has some FFT pseudocode in other chapters that could be used for this. 1000 digits is "small" for Schönhage

The naive way would be to linearly iterate the range and multiply with each number in the range. Example: Array: {1,2,3,4,5,6,7,8,9,10}; Multiply index 3 to index 8 with 2. Assuming one based index. Result array should be : {1,2,6,8,10,12,14,16,9,10}; I know that Binary indexed tree can be used for the 'sum' part. How can I efficiently multiply a given range with a number? If you want to actually modify the array, you can't do better than the naive linear algorithm: you have to iterate the entire range and modify each index accordingly. If you mean something like, you have update operations