Design Optimization

        s.m = GEKKO(remote=False)

(:sourceend:)

In Python, parallelization is accomplished with multithreading. The following example shows an example of how to create and run a program with 10 threads that each print a message.

(:toggle hide single_gekko button show="Show Single Threaded GEKKO Python":) (:div id=single_gekko:) (:source lang=python:) import numpy as np from gekko import GEKKO

1. Optimize at mesh points

x = np.arange(20.0, 30.0, 2.0) y = np.arange(30.0, 50.0, 4.0) amg, bmg = np.meshgrid(x, y)

1. Initialize results array

obj = np.empty_like(amg)

m = GEKKO(remote=False) objective = float('NaN')

a,b = m.Array(m.FV,2)

1. model variables, equations, objective

x1 = m.Var(1,lb=1,ub=5) x2 = m.Var(5,lb=1,ub=5) x3 = m.Var(5,lb=1,ub=5) x4 = m.Var(1,lb=1,ub=5) m.Equation(x1*x2*x3*x4>=a) m.Equation(x1**2+x2**2+x3**2+x4**2==b) m.Minimize(x1*x4*(x1+x2+x3)+x3) m.options.SOLVER = 1 # APOPT solver

1. Calculate obj at all meshgrid points

for i in range(amg.shape[0]):

    for j in range(bmg.shape[1]):
        a.MEAS = amg[i,j]
        b.MEAS = bmg[i,j]

        m.solve(disp=False)

        obj[i,j] = m.options.OBJFCNVAL
        print(amg[i,j],bmg[i,j],obj[i,j])

1. plot 3D figure of results

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm import numpy as np

fig = plt.figure() ax = fig.gca(projection='3d') surf = ax.plot_surface(amg, bmg, obj, rstride=1, cstride=1, cmap=cm.coolwarm, vmin = 10, vmax = 25, linewidth=0, antialiased=False) ax.set_xlabel('a') ax.set_ylabel('b') ax.set_zlabel('obj') plt.show() (:divend:)

(:toggle hide gekko button show="Show Multithreaded GEKKO Python":)

        s.m.Minimize(s.m.x1*s.m.x4*(s.m.x1+s.m.x2+s.m.x3)+s.m.x3)

(:html:) <iframe width="560" height="315" src="https://www.youtube.com/embed/z2gNLjJs6EM" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> (:htmlend:)

Programs can run on multiple CPU cores or on heterogeneous networks and platforms with parallelization. In this example application, we solve a series of optimization problems using Linux and Windows servers using Python multi-threading. The optimization problems are initialized sequentially, computed in parallel, and returned asynchronously to the MATLAB or Python script.

Programs can run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script.

        self.m.cleanup()

(:toggle hide multithread button show="Show Multithreading in Python":) (:div id=multithread:)

(:divend:)

(:toggle hide gekko button show="Show GEKKO Python Solution":) (:div id=gekko:) (:source lang=python:) import numpy as np import threading import time, random from gekko import GEKKO

class ThreadClass(threading.Thread):

    def __init__(self, id, server, ai, bi):
        s = self
        s.id = id
        s.server = server
        s.m = GEKKO()
        s.a = ai
        s.b = bi
        s.objective = float('NaN')

        # initialize variables
        s.m.x1 = s.m.Var(1,lb=1,ub=5)
        s.m.x2 = s.m.Var(5,lb=1,ub=5)
        s.m.x3 = s.m.Var(5,lb=1,ub=5)
        s.m.x4 = s.m.Var(1,lb=1,ub=5)

        # Equations
        s.m.Equation(s.m.x1*s.m.x2*s.m.x3*s.m.x4>=s.a)
        s.m.Equation(s.m.x1**2+s.m.x2**2+s.m.x3**2+s.m.x4**2==s.b)

        # Objective
        s.m.Obj(s.m.x1*s.m.x4*(s.m.x1+s.m.x2+s.m.x3)+s.m.x3)

        # Set global options
        s.m.options.IMODE = 3 # steady state optimization
        s.m.options.SOLVER = 1 # APOPT solver

        threading.Thread.__init__(s)

    def run(self):

        # Don't overload server by executing all scripts at once
        sleep_time = random.random()
        time.sleep(sleep_time)

        print('Running application ' + str(self.id) + '\n')

        # Solve
        self.m.solve(disp=False)

        # Results
        #print('')
        #print('Results')
        #print('x1: ' + str(self.m.x1.value))
        #print('x2: ' + str(self.m.x2.value))
        #print('x3: ' + str(self.m.x3.value))
        #print('x4: ' + str(self.m.x4.value))

        # Retrieve objective if successful
        if (self.m.options.APPSTATUS==1):
            self.objective = self.m.options.objfcnval
        else:
            self.objective = float('NaN')

1. Select server

server = 'https://byu.apmonitor.com'

1. Optimize at mesh points

x = np.arange(20.0, 30.0, 2.0) y = np.arange(30.0, 50.0, 2.0) a, b = np.meshgrid(x, y)

1. Array of threads

threads = []

1. Calculate objective at all meshgrid points
2. Load applications

id = 0 for i in range(a.shape[0]):

    for j in range(b.shape[1]):
        # Create new thread
        threads.append(ThreadClass(id, server, a[i,j], b[i,j]))
        # Increment ID
        id += 1

1. Run applications simultaneously as multiple threads
2. Max number of threads to run at once

max_threads = 8 for t in threads:

    while (threading.activeCount()>max_threads):
        # check for additional threads every 0.01 sec
        time.sleep(0.01)
    # start the thread
    t.start()

1. Check for completion

mt = 3.0 # max time it = 0.0 # incrementing time st = 1.0 # sleep time while (threading.activeCount()>=1):

    time.sleep(st)
    it = it + st
    print('Active Threads: ' + str(threading.activeCount()))
    # Terminate after max time
    if (it>=mt):
        break

1. Wait for all threads to complete
2. for t in threads:
3. t.join()
4. print('Threads complete')
5. Initialize array for objective

obj = np.empty_like(a)

1. Retrieve objective results

id = 0 for i in range(a.shape[0]):

    for j in range(b.shape[1]):
        obj[i,j] = threads[id].objective
        id += 1

1. plot 3D figure of results

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm import numpy as np

fig = plt.figure() ax = fig.gca(projection='3d') surf = ax.plot_surface(a, b, obj, rstride=1, cstride=1, cmap=cm.coolwarm, vmin = 12, vmax = 22, linewidth=0, antialiased=False) ax.set_xlabel('a') ax.set_ylabel('b') ax.set_zlabel('obj') ax.set_title('Multi-Threaded GEKKO') plt.show() (:sourceend:) (:divend:)

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script.

Multithreading in Python

In Python, parallelization is accomplished with multithreading. The following example shows how to create and run a program with 10 threads that each print a message.

        print("ID => s completes at %s\n" %               (self.id, self.getName(), now))

    print('Active threads: ' + str(threading.activeCount()))

print('All threads: \n') print(threading.enumerate())

print('Threads complete')

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script. In Python, parallelization is accomplished with multi-threading.

(:source lang=python:) import threading import datetime import time, random

class MyThread(threading.Thread):

    def __init__(self, id):
        self.id = id
        self.delay = random.random()
        threading.Thread.__init__(self)
    def run(self):
        time.sleep(self.delay)
        now = datetime.datetime.now()
        print "ID => s completes at %s\n" %               (self.id, self.getName(), now)

1. Start threads

threads = [] for i in range(10):

    threads.append(MyThread(i))
    threads[i].start()
    print 'Active threads: ' + str(threading.activeCount())

1. Print threads

print 'All threads: \n' print threading.enumerate()

1. Wait for all threads to complete

for t in threads:

    t.join()

print 'Threads complete' (:sourceend:)

The next step is to embed a simple Nonlinear Programming (NLP) problem into the multi-threaded application. The tutorial examples are available for download below:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script. The tutorial begins with a simple Nonlinear Programming problem. The tutorial examples are available for download below:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB interface. The tutorial begins with a simple Nonlinear Programming problem. The tutorial examples are available for download below:

• Parallel Computing Example Files for APM Python (.zip)
• 

• Parallel Computing Example Files for APM MATLAB (.zip)
• 

<iframe width="560" height="315" src="https://www.youtube.com/embed/Hr-d_yHKPn4?rel=0" frameborder="0" allowfullscreen></iframe>

• Parallel Computing Example Files (.zip)

(:html:) <iframe width="560" height="315" src="https://www.youtube.com/embed/GB0NYz-k8ZM?rel=0" frameborder="0" allowfullscreen></iframe> (:htmlend:)

(:title Parallel Computing in Optimization:) (:keywords parallel computing, mathematical modeling, nonlinear, optimization, engineering optimization, interior point, active set, differential, algebraic, modeling language, university course:) (:description Tutorial on using MATLAB to solve parallel computing optimization applications.:)

Parallel Computing

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB interface. The tutorial begins with a simple Nonlinear Programming problem that is formulated in two different ways. The tutorial example and other files are available for download below:

• Parallel Computing Example Files (.zip)

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