ipopt

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

问题 Any example for multi-objective optimization in Pyomo? I am trying to minimize 4 Objectives (Non Linear) and I would like to use pyomo and ipopt. Have also access to Gurobi. I want to see even very simple example where we try to optimize for two or more objective (one minimization and one maximization) for a list of decision variables (not just one dimension but maybe a vector). Pyomo book that I have (https://link.springer.com/content/pdf/10.1007%2F978-3-319-58821-6.pdf) does not provide a

问题 my script is: from __future__ import division import numpy import scipy from pyomo.environ import * from pyomo.dae import * from pyomo.opt import SolverFactory m=ConcreteModel() m.x3=Var(within=NonNegativeReals) m.u=Var(within=NonNegativeReals) def _con(m): return m.x3 >=3 m.con=Constraint(rule=_con) def _con2(m): return 4 >= m.u >=1 m.con2=Constraint(rule=_con2) m.obj=Objective(expr=m.x3*m.u) opt = SolverFactory("Ipopt", executable = "/Ipopt-3.12.6/bin/ipopt") results = opt.solve(m) results

问题 I am using IPOPT implemented through OpenMDAO and am having some trouble understanding and controlling the stopping criteria. Here is what I'm experiencing specifically: Initially, IPOPT is able to find a solution that appears to be much better, although constraints are violated slightly (intuition tells me that adjusting a few parameters would likely bring it into the feasible region). From this discussion I understand that "linear or nonlinear equality or inequality constraint will not

问题 How can I install ipopt solver to use it with pyomo in ubuntu??? I have already download the zip file of ipopt but I don'n find any specific instructions how to install it and use it with pyomo. 回答1: If you downloaded a binary then you just have to make sure that the ipopt executable is in your PATH environment variable. If you are trying to install from source code you should follow the instructions here. Your last option is to use anaconda to install an ipopt binary using the command: conda

问题 I'm trying to solve the following problem: I have a lot (~80000) surface patches of an organ that's growing. I measure each of its areas over time (18 time-points) and want to fit a growth curve to it (bi-logistic model, eg. just the sum of two logistic functions bcs. there are two 'growth spurts' happening in the observed period). I have box constraints to ensure that the exponential terms don't explode and a linear constraint that one growth spurt has to happen after the other. Also, in

问题 I am trying to solve an optimisation problem consisting in finding the global maximum of a high dimensional (10+) monotonic function (as in monotonic in every direction). The constraints are such that they cut the search space with planes. I have coded the whole thing in pyomo and I am using the ipopt solver. In most cases, I am confident it converges successfully to the global optimal. But if I play a bit with the constraints I see that it sometimes converges to a local minima. It looks like

问题 I’m pretty new to optimization field, so forgive me if my question is too simple. I ran an optimization using Scipy (method SLSQP) and another one using Pyomo (IPOPT solver). Pyomo runs in less than one minute and Scipy takes 4 hours. Both have the same inputs, constraints and objective function. However, I got different results and my final results are 3% lower in Pyomo. There is no constraint violation, so I wonder if there is anything that happens under the hood to justify this difference?

问题 I'm trying to link Ipopt with Intel MKL (instructions). Intel's Link Advisor suggests: Link line: -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_core.a ${MKLROOT}/lib/intel64/libmkl_intel_thread.a -Wl,--end-group -lpthread -lm -ldl Compiler options: -DMKL_ILP64 -qopenmp -I${MKLROOT}/include I try to configure Ipopt with: ../configure CXX=icpc CC=icc F77=ifort --with-blas=" -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib

问题 I am running an optimization problem using pyomo's ipopt solver. My problem is sort of complicated, and it is declared infeasible by IPOPT. I will not post the entire problem unless needed. But, one thing to note is, I am providing a warm start for the problem, which I thought would help prevent infeasibility from rearing its ugly head. Here's the output from pyomo and ipopt when I set tee=True inside of the solver: Ipopt 3.12.4: ***************************************************************