mathematical-optimization

来源： https://stackoverflow.com/questions/58646404/how-to-solve-absolute-value-abs-objective-with-python-gekko

来源： https://stackoverflow.com/questions/58646404/how-to-solve-absolute-value-abs-objective-with-python-gekko

问题 I have a dataset with 50,000 orders. Each order has ~20 products. Product volume and weight are present (as well as x,y,z dimensions). I have shipping boxes of constant volume V_max and maximum weight capacity of W_max. Per order I want to minimize the number of boxes used under the constraint that V < V_max, and W < W_max. In searching the web I have come across many binpacking algorithms, but none of them seem to do the trick. Does anyone know of an elegant (and fast) python algorithm for

问题 I have a large data frame with more than 100 000 records where the values are sorted For example, consider the following dummy data set df <- data.frame(values = c(1,1,2,2,3,4,5,6,6,7)) I want to create 3 groups of above values (in sequence only) such that the sum of each group is more or less the same So for the above group, if I decide to divide the sorted df in 3 groups as follows, their sums will be 1. 1 + 1 + 2 +2 + 3 + 4 = 13 2. 5 + 6 = 11 3. 6 + 7 = 13 How can create this optimization

问题 I have a large data frame with more than 100 000 records where the values are sorted For example, consider the following dummy data set df <- data.frame(values = c(1,1,2,2,3,4,5,6,6,7)) I want to create 3 groups of above values (in sequence only) such that the sum of each group is more or less the same So for the above group, if I decide to divide the sorted df in 3 groups as follows, their sums will be 1. 1 + 1 + 2 +2 + 3 + 4 = 13 2. 5 + 6 = 11 3. 6 + 7 = 13 How can create this optimization

问题 I have been trying to use 'cbc' solver in pyomo as following: """ import pyomo.environ as pe solver = pe.SolverFactory('cbc') result = solver.solve(m) """ Currently, I have been relying on the embedded solver with pyomo installation, which is 'glpk', and I have found that 'cbc' can do better job in many circumstances. I am using: python 3.XX Anaconda environment Windows 10 Pyomo (installed via Conda) It would be great to upgrade the ability of pyomo by using advanced solver! 回答1: You will

问题 I've attempted to implement the Calafiore (2002) method of fitting ellipsoids to data using CVXPY as a wrapper to SCS to perform the semi-definite programming. When I run this implementation, I get decent agreement with the first ellipsoid of Section IVB (Fig 6a). However, I don't get numeric agreement with the second ellipsoid of that section (Fig 6b), although the results are visually close. Worse, when I try to fit a very well defined ellipse (the points below) I get an obviously poor fit

问题 I've attempted to implement the Calafiore (2002) method of fitting ellipsoids to data using CVXPY as a wrapper to SCS to perform the semi-definite programming. When I run this implementation, I get decent agreement with the first ellipsoid of Section IVB (Fig 6a). However, I don't get numeric agreement with the second ellipsoid of that section (Fig 6b), although the results are visually close. Worse, when I try to fit a very well defined ellipse (the points below) I get an obviously poor fit

问题 I am trying to model the operation of a system component, the component will have two operating modes, let's call them 1 and 2, plus the idle mode 0 There is no limit on idling, but each operating mode will last for exactly 3 time-series points, so x_{i}= 1 means x_{i+1} = x_{i+2} = 1 (cannot post images, please use the link below for the equation) operation mode 1 same goes for operating mode 2. For example. 011102220 is valid, but 01110220 is not. 111111 or 222222 are not valid, but this is

问题 I have a Mixed Integer Programming problem. I can use JuMP to find the optimal solution. But how can I find the second best solution? Or the third-best etc. This potentially might be another equally optimal solution, or it might be a worse solution, or it might be :Infeasible -- there might be no most solutions. I know for a TSP-like problem, I can find additional solutions by progressively removing links that are on the optimal path (I.e setting the distances between some of the cities to be