mathematical-optimization

Can the nlm function be used for optimization with multiple variables? How would that work? For instance: I want to find x and y so that f(x,y) is minimized. How would the nlm function work? something like that?: nlm(f,c(0.1,0.1)) Make a function that receives a vector: f <- function(X) { x <- X[1] y <- X[2] (x-3.14)^2 + (y-6.28)^2 } nlm(f,c(0.1,0.1)) 来源： https://stackoverflow.com/questions/40620277/r-how-can-the-nlm-function-be-used-for-optimization-with-multiple-variables

I am trying to find maximum values for below objective function: objective <-function(bid,revenue,click,cost) { revenue_2 <- sum((revenue / cost)* (bid*click*bid*(cost/click) / cost)^(-0.2*revenue/cost)* (bid*click)*bid*(cost/click)) return(-revenue_2) } subject to roas_2 <- function(bid, revenue,click,cost) { revenue_2 <- ((revenue / cost)* (bid*click*bid*(cost/click) / cost)^(-0.2*revenue/cost))* (bid*click)*bid*(cost/click) cost_2 <- (bid*click)*bid*(cost/click) roas_2 <- (sum(revenue_2)/sum(cost_2)) -1.2 return(-roas_2) } where my values are as follows: click <- c(123565, 94434, 79345,

I'm trying to run a logistic regression in statsmodels on a large design matrix (~200 columns). The features include a number of interactions, categorical features and semi-sparse (70%) integer features. Although my design matrix is not actually ill-conditioned, it seems to be somewhat close (according to numpy.linalg.matrix_rank , it is full-rank with tol=1e-3 but not with tol=1e-2 ). As a result, I'm struggling to get logistic regression to converge with any of the methods in statsmodels. Here's what I've tried so far: method='newton' : Did not converge after 1000 iterations; raised a

I'm using scipy.optimize.minimize to solve a complex reservoir optimization model (SQSLP and COBYLA as the problem is constrained by both bounds and constraint equations). There is one decision variable per day (storage), and releases from the reservoir are calculated as a function of change in storage, within the objective function. Penalties based on releases and storage penalties are then applied with the goal of minimizing penalties (the objective function is a summation of all penalties). I've added some constraints within this model to limit the change in storage to the physical system

I have an array ( arr ) of elements, and a function ( f ) that takes 2 elements and returns a number. I need a permutation of the array, such that f(arr[i], arr[i+1]) is as little as possible for each i in arr . (and it should loop, ie. it should also minimize f(arr[arr.length - 1], arr[0]) ) Also, f works sort of like a distance, so f(a,b) == f(b,a) I don't need the optimum solution if it's too inefficient, but one that works reasonable well and is fast since I need to calculate them pretty much in realtime (I don't know what to length of arr is, but I think it could be something around 30)