mathematical-optimization

问题 I am interested in the performance of Pyomo to generate an OR model with a huge number of constraints and variables (about 10e6). I am currently using GAMS to launch the optimizations but I would like to use the different python features and therefore use Pyomo to generate the model. I made some tests and apparently when I write a model, the python methods used to define the constraints are called each time the constraint is instanciated. Before going further in my implementation, I would

问题 I have a data frame with on the order of 20 numeric columns, each containing significant amounts of NA values. I would like to select a subset of these columns that will give me the most rows containing zero NA values. An exhaustive search would take a lot of computing time--is there a better way to get an approximation? Here is an example with a smaller data frame (completely arbitrary): set.seed(2) foo = as.data.frame(matrix(rnorm(200), nr = 20)) foo[sapply(foo, function(x) x > abs(x[1]))]

问题 I feel like I missed something very obvious but after an hour of fiddling/googling I cannot get this to work. Code: #Generate data from exponential model xdata<-seq_len(100) ydata<-2*exp(-2*(xdata+rnorm(100))) #Fit exponential model to data firstorder<-function(C0,k){ ynew<-C0*exp(-k*xdata) RMSE<-sum((ynew-ydata)^2,na.rm=TRUE) return(RMSE) } #Initial parameter values params<-c(1,1) #Optimize optimx(params,firstorder) Error in optimx.check(par, optcfg$ufn, optcfg$ugr, optcfg$uhess, lower, :

问题 I feel like I missed something very obvious but after an hour of fiddling/googling I cannot get this to work. Code: #Generate data from exponential model xdata<-seq_len(100) ydata<-2*exp(-2*(xdata+rnorm(100))) #Fit exponential model to data firstorder<-function(C0,k){ ynew<-C0*exp(-k*xdata) RMSE<-sum((ynew-ydata)^2,na.rm=TRUE) return(RMSE) } #Initial parameter values params<-c(1,1) #Optimize optimx(params,firstorder) Error in optimx.check(par, optcfg$ufn, optcfg$ugr, optcfg$uhess, lower, :

问题 I want to solve the following non-linear system of equations. Notes the dot between a_k and x represents dot product . the 0 in the first equation represents 0 vector and 0 in the second equation is scaler 0 all the matrices are sparse if that matters. Known K is an n x n (positive definite) matrix each A_k is a known (symmetric) matrix each a_k is a known n x 1 vector N is known (let's say N = 50). But I need a method where I can easily change N. Unknown (trying to solve for) x is an n x 1 a

问题 I want to solve the following non-linear system of equations. Notes the dot between a_k and x represents dot product . the 0 in the first equation represents 0 vector and 0 in the second equation is scaler 0 all the matrices are sparse if that matters. Known K is an n x n (positive definite) matrix each A_k is a known (symmetric) matrix each a_k is a known n x 1 vector N is known (let's say N = 50). But I need a method where I can easily change N. Unknown (trying to solve for) x is an n x 1 a

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 4 years ago . I have the following data frames: Required <- data.table( Country=c("AT", "BE", "BG", "CY"),Mat1=c(0,5,0,5),Mat2=c(0,3,2,0),Mat3=c(10,2,0,12)) Supplied <- data.table( Country=c("AT", "BE", "BG", "CY"),Mat1=c(0,4,0,10),Mat2=c(20,20,20,0),Mat3=c(8,10,0,10)) > Required Country Mat1 Mat2 Mat3 1: AT 0 0

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'm working on a physics engine and feel it would help having a better understanding of the speed and performance effects of performing many simple or complex math operations. A large part of a physics engine is weeding out the unnecessary computations, but at what point are the computations small enough that a comparative checks aren't necessary? eg: Testing if two line segments intersect. Should there be check on if they're near each other before just going straight into the simple math, or

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