Models in this form are actually called bilinear optimization problems. The typical approach to linearizing bilinear terms is through something called the McCormick envelope. Consider variables x and y, where you want x*y in the objective of your maximization problem. If we assume x and y are bounded by xL <= x <= xU and … Read more
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 commercial solvers, gurobi, cplex and Dash unless money is a big issue for … Read more
You need preserve index values by reset_index and parameter id_vars: df2 = pd.melt(df.reset_index(), id_vars=”index”,value_vars=[‘asset1′,’asset2′]) print (df2) index variable value 0 coper1 asset1 1 1 coper2 asset1 3 2 coper3 asset1 5 3 coper1 asset2 2 4 coper2 asset2 4 5 coper3 asset2 6 Then pivot working nice: print(df2.pivot(index=’index’,columns=”variable”, values=”value”)) variable asset1 asset2 index coper1 1 … Read more
I personally found GLPK better (i.e. faster) than LP_SOLVE. It supports various file formats, and a further advantage is its library interface, which allows smooth integration with your application.
Pulp is a python modeling interface that hooks up to solvers like CBC(open source), CPLEX (commercial), Gurobi(commercial), XPRESS-MP(commercial) and YALMIP(open source). You can also use Pyomo to model the optimization problem and then call an external solver, namely CPLEX, Gurobi GLPK and the AMPL solver library. You can also call GLPK from GLPK/Python, PyGLPK or … Read more
Thanks @Ole V.V for the solution.I ran your code against below double a = 10000;//can be 40,000,000 double n = 50000;//can b 5,000,000,000 double r = a * 100 / n;//should be between a given range However ,in the output “r” is always constant(20.0)for each and every iteration We actually need r between an given … Read more
One option is to use the APIs that commercial solvers like CPLEX and Gurobi provide for C/C++. Essentially, these APIs let you build the model in logical chunks (objective function, constraints, etc.). The APIs do the work of translating the logic of the model to the matrices and vectors that the solver actually needs in … Read more