mathematical-optimization

Does anybody know how to find the local maxima in a grayscale IPL_DEPTH_8U image using OpenCV? HarrisCorner mentions something like that but I'm actually not interested in corners ... Thanks! I think you want to use the MinMaxLoc(arr, mask=NULL)-> (minVal, maxVal, minLoc, maxLoc) Finds global minimum and maximum in array or subarray function on you image A pixel is considered a local maximum if it is equal to the maximum value in a 'local' neighborhood. The function below captures this property in two lines of code. To deal with pixels on 'plateaus' (value equal to their neighborhood) one can

I am looking for an optimisation routine within scipy/numpy which could solve a non-linear least-squares type problem (e.g., fitting a parametric function to a large dataset) but including bounds and constraints (e.g. minima and maxima for the parameters to be optimised). At the moment I am using the python version of mpfit (translated from idl...): this is clearly not optimal although it works very well. An efficient routine in python/scipy/etc could be great to have ! Any input is very welcome here :-) thanks! scipy.optimize.least_squares in scipy 0.17 (January 2016) handles bounds; use that

I'm trying to solve integer programming problems. I've tried both use SCIP and LPSolve For example, given the final values of A and B, I want to solve for valA in the following C# code: Int32 a = 0, b = 0; a = a*-6 + b + 0x74FA - valA; b = b/3 + a + 0x81BE - valA; a = a*-6 + b + 0x74FA - valA; b = b/3 + a + 0x81BE - valA; // a == -86561, b == -32299 Which I implemented as this integer program in lp format (the truncating division causes a few complications): min: ; +valA >= 0; +valA < 92; remAA_sign >= 0; remAA_sign <= 1; remAA <= 2; remAA >= -2; remAA +2 remAA_sign >= 0; remAA +2 remAA_sign <

Does anyone know of such a library that performs mathematical optimization (linear programming, convex optimization, or more general types of problems)? I'm looking for something like MATLAB, but with the ability to handle larger problems. Do I have to write my own implementations, or buy one of those commercial products (CPLEX and the like)? A good answer is dependent on what you mean by "convex" and "more general" If you are trying to solve large or challenging linear or convex-quadratic optimization problems (especially with a discrete component to them), then it's hard to beat the main

Approach 1: C(n,r) = n!/(n-r)!r! Approach 2: In the book Combinatorial Algorithms by wilf , i have found this: C(n,r) can be written as C(n-1,r) + C(n-1,r-1) . e.g. C(7,4) = C(6,4) + C(6,3) = C(5,4) + C(5,3) + C(5,3) + C(5,2) . . . . . . . . After solving = C(4,4) + C(4,1) + 3*C(3,3) + 3*C(3,1) + 6*C(2,1) + 6*C(2,2) As you can see, the final solution doesn't need any multiplication. In every form C(n,r), either n==r or r==1. Here is the sample code i have implemented: int foo(int n,int r) { if(n==r) return 1; if(r==1) return n; return foo(n-1,r) + foo(n-1,r-1); } See output here. In the