optimization

问题 I am writing a program that gives me an overflow error. I realized that the cause of that was due to my input which could do inputs till 2147483646. I figured this using the sys.maxint. Anything beyond that gave me an overflow error. How I can take in inputs for large values? For value, 2147483646 my system hangs. How to deal with such an error. The statement of the problem here is given, A number is called lucky if the sum of its digits, as well as the sum of the squares of its digits is a

问题 Essentially, I want to use the $foo = new RecursiveIteratorIterator(new RecursiveArrayIterator($haystack)); Methodology, but instead of returning a flat array for foreach() ing through, keep the structure but only return a single great-grand-child node and it's parent nodes. Is this possible in PHP? I've been tasked with optimising some of my company's (horrific) codebase. I found a function that recurses through an array, searching for a key. I can't replace this with a simple array_search()

问题 I have a list of customers and each of them can be "activated" in four different ways: n= 1000 df = pd.DataFrame(list(range(0,n)), columns = ['Customer_ID']) df['A'] = np.random.randint(2, size=n) df['B'] = np.random.randint(2, size=n) df['C'] = np.random.randint(2, size=n) each customer can be activated either on "A" or on "B" or on "C" and only if the Boolean related to the type of activation is equal to 1. In input i have the count of the final activations. es: Target_A = 500 Target_B =

问题 So I have 3 datasets as follows, Cost data: Vcost Out[325]: P1 P2 P3 Vendors\Product List V1 0.204403 0.208178 0.198216 V2 0.220126 0.213755 0.198991 V3 0.204403 0.191450 0.203258 Risk data: Vrisk Out[326]: P1 P2 P3 Vendors\Product List V1 0.198598 0.210145 0.198157 V2 0.172897 0.178744 0.193548 V3 0.219626 0.200483 0.205069 Decision variables data: Vdecision Out[327]: P1 P2 P3 Vendors\Product List V1 a b c V2 f g h V3 k l m My objective is to minimize 0.71*Cost*x + 0.29*Risk*x subjected to

问题 I've found recently that for some types of financial calculations that the following pattern is much easier to follow and test especially in situations where we may need to get numbers from various stages of the computation. public class nonsensical_calculator { ... double _rate; int _term; int _days; double monthlyRate { get { return _rate / 12; }} public double days { get { return (1 - i); }} double ar { get { return (1+ days) /(monthlyRate * days) double bleh { get { return Math.Pow(ar -

问题 While doing GP regression in GPflow 2.0, I want to set hard bounds on lengthscale (i.e. limiting lengthscale optimization range). Following this thread (Setting hyperparameter optimization bounds in GPflow 2.0), I constructed a TensorFlow Bijector chain (see bounded_lengthscale function below). However, the bijector chain below does not prevent the model from optimizing outside the supposed bounds. What do I need to change to make the bounded_lengthscale function put hard bounds on

问题 I have an optimization problem to solve in order to filter an image. I created a Linear Equation of the problem which deals with Sparse Matrices. At first I will show the problem. First, the Laplacian (Adjacency) matrix of the problem: The matrix Dx / Dy is the forward difference operator -> Hence its transpose is the backward difference operator. The matrix Ax / Ay is diagonal matrix with weights which are function of the gradient of the image (Point wise, namely the value depends only on

问题 The real code I want to optimize is too complicated to be included here, so here is a simplified example: def enumerate_paths(n, k): """ John want to go up a flight of stairs that has N steps. He can take up to K steps each time. This function enumerate all different ways he can go up this flight of stairs. """ paths = [] to_analyze = [(0,)] while to_analyze: path = to_analyze.pop() last_step = path[-1] if last_step >= n: # John has reach the top paths.append(path) continue for i in range(1,

问题 Partially related to my other question here. In my case the 'original' aim was to choose n=50 objects out of N=292, such that the sum of all pairwise distances between the chosen objects is maximized (maxsum or p-dispersion sum). Thanks to the users who provided advice, I did some further reading, and now I understand that the problem is indeed quadratic in its simplest form, and a solver like CPLEX may be able to solve it. However, this article by Kuby points out that the maxsum results does

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 3 years ago . I have an export job migrating data from an old database into a new database. The problem I'm having is that the user population is around 3 million and the job takes a very long time to complete (15+ hours). The machine I am using only has 1 processor so I'm not sure if threading