Optimization deals with selecting the simplest option among a number of possible choices that are feasible or do not violate constraints. Python is used to optimize parameters in a model to best fit data, increase profitability of a possible engineering style, or meet another form of objective which will be described mathematically with variables and equations.

pyOpt is a Python-based package for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. Python programming uses object-oriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem.

All optimisation downside solvers inherit from the Optimizer abstract category. The category attributes include the solver name (name), an optimizer kind symbol (category), and dictionaries that contain the solver setup parameters (options) and message output settings (informs). The class provides ways to check and alter default solver parameters (getOption, setOption), as well as a method that runs the solver for a given optimisation problem (solve).

Optimization solver

A number of constrained optimization solvers are designed to solve the general nonlinear optimization problem.

1. PSQP: This optimizer is a preconditioned sequential quadratic programming algorithm. This optimizer implements a sequential quadratic programming method with a BFGS variable metric update.
2. SLSQP: This optimizer is a sequential least squares programming algorithm. SLSQP uses the Han–Powell quasi-Newton method with a BFGS update of the B-matrix and an L1-test function in the step-length algorithm. The optimizer uses a slightly modified version of Lawson and Hanson’s NNLS nonlinear least-squares solver.
3. CONMIN: This optimizer implements the method of feasible directions. CONMIN solves the nonlinear programming problem by moving from one feasible point to an improved one by choosing at each iteration a feasible direction and step size that improves the objective function.
4. COBYLA: It is an implementation of Powell’s nonlinear derivative–free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust–region algorithm that employs linear approximations to the objective and constraint functions.
5. SOLVOPT: SOLVOPT is a modified version of Shor’s r–algorithm with space dilation to find a local minimum of nonlinear and non–smooth problems.
6. KSOPT: This code reformulates the constrained problem into an unconstrained one using a composite Kreisselmeier–Steinhauser objective function to create an envelope of the objective function and set of constraints. The envelope function is then optimized using a sequential unconstrained minimization technique.
7. NSGA2: This optimizer is a non-dominating sorting genetic algorithm that solves non-convex and non-smooth single and multiobjective optimization problems.
8. ALGENCAN: It solves the general non-linear constrained optimization problem without resorting to the use of matrix manipulations. It uses instead an Augmented Lagrangian approach which is able to solve extremely large problems with moderate computer time.
9. FILTERSD: It use of a Ritz values approach Linear Constraint Problem solver. Second derivatives and storage of an approximate reduced Hessian matrix is avoided using a limited memory spectral gradient approach based on Ritz values.

To solve an optimization problem with pyOpt an optimizer must be initialized. The initialization of one or more optimizers is independent of the initialization of any number of optimization problems. To initialize SLSQP, which is an open-source, sequential least squares programming algorithm that comes as part of the pyOpt package, use:

>>> slsqp = pyOpt.SLSQP()

This initializes an instance of SLSQP with the default options. The setOption method can be used to change any optimizer specific option, for example the internal output flag of SLSQP:

>>> slsqp.setOption('IPRINT', -1)

Now Schittkowski’s constrained problem can be solved using SLSQP and for example, pyOpt’s automatic finite difference for the gradients:

>>> [fstr, xstr, inform] = slsqp(opt_prob,sens_type='FD')

By default, the solution information of an optimizer is also stored in the specific optimization problem. To output solution to the screen one can use:

>>> print opt_prob.solution(0)

Example:

The problem is taken from the set of nonlinear programming examples by Hock and Schittkowski and it is defined as

=======================================================================

      min            − x1x2x3

     x1,x2,x3

subjected to     x1 + 2x2 + 2x3 − 72 ≤ 0

                        − x1 − 2x2 − 2x3 ≤ 0

                        0 ≤ x1 ≤ 42

                        0 ≤ x2 ≤ 42

                        0 ≤ x3 ≤ 42

The optimum of this problem is at (x1∗ , x2∗ , x3* ) = (24, 12, 12), with an objective function value of f ∗ = −3456, and constraint values g (x∗ ) = (0, −72).

#======================================================================

# Standard Python modules

#======================================================================

import os, sys, time

import pdb

#======================================================================

# Extension modules

#======================================================================

#from pyOpt import *

from pyOpt import Optimization

from pyOpt import PSQP

from pyOpt import SLSQP

from pyOpt import CONMIN

from pyOpt import COBYLA

from pyOpt import SOLVOPT

from pyOpt import KSOPT

from pyOpt import NSGA2

from pyOpt import ALGENCAN

from pyOpt import FILTERSD

#======================================================================

def objfunc(x):

    f = -x[0]*x[1]*x[2]

    g = [0.0]*2

    g[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0

    g[1] = -x[0] - 2.*x[1] - 2.*x[2]

    fail = 0

    return f,g, fail  

#======================================================================

# Instantiate Optimization Problem

opt_prob = Optimization('Hock and Schittkowski Constrained Problem',objfunc)

opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addObj('f')

opt_prob.addCon('g1','i')

opt_prob.addCon('g2','i')

print opt_prob

# Instantiate Optimizer (PSQP) & Solve Problem

psqp = PSQP()

psqp.setOption('IPRINT',0)

psqp(opt_prob,sens_type='FD')

print opt_prob.solution(0)

# Instantiate Optimizer (SLSQP) & Solve Problem

slsqp = SLSQP()

slsqp.setOption('IPRINT',-1)

slsqp(opt_prob,sens_type='FD')

print opt_prob.solution(1)

# Instantiate Optimizer (CONMIN) & Solve Problem

conmin = CONMIN()

conmin.setOption('IPRINT',0)

conmin(opt_prob,sens_type='CS')

print opt_prob.solution(2)

# Instantiate Optimizer (COBYLA) & Solve Problem

cobyla = COBYLA()

cobyla.setOption('IPRINT',0)

cobyla(opt_prob)

print opt_prob.solution(3)

# Instantiate Optimizer (SOLVOPT) & Solve Problem

solvopt = SOLVOPT()

solvopt.setOption('iprint',-1)

solvopt(opt_prob,sens_type='FD')

print opt_prob.solution(4)

# Instantiate Optimizer (KSOPT) & Solve Problem

ksopt = KSOPT()

ksopt.setOption('IPRINT',0)

ksopt(opt_prob,sens_type='FD')

print opt_prob.solution(5)

# Instantiate Optimizer (NSGA2) & Solve Problem

nsga2 = NSGA2()

nsga2.setOption('PrintOut',0)

nsga2(opt_prob)

print opt_prob.solution(6)

# Instantiate Optimizer (ALGENCAN) & Solve Problem

algencan = ALGENCAN()

algencan.setOption('iprint',0)

algencan(opt_prob)

print opt_prob.solution(7)

# Instantiate Optimizer (FILTERSD) & Solve Problem

filtersd = FILTERSD()

filtersd.setOption('iprint',0)

filtersd(opt_prob)

print opt_prob.solution(8)

Solving non-linear global optimization problems could be tedious task sometimes. If the problem is not that complex then general purpose solvers could work. However, as the complexity of problem increases, general purpose global optimizers start to take time. That is when need to create your problem specific fast and direct global optimizer’s need arises.

We have an specialized team with PHD holders and coders to design and develop customized global optimizers. If you need help with one, please feel free to send your queries to us.

We first understand the problem and data by visualizing it. After that we create a solution to your needs.

Please do read to understand what a solver is and how it works - If you want to create your own simple solver. This is not exactly how every solver works, however, this will give you a pretty solid idea of what is a solver and how it is supposed to work.

Over the last year or so, programming languages have regularly been prefixed with a curious word: modern, Modern Java. But what exactly does modern mean when used in this way?

When someone talks about modern languages, they're really just talking about how refined, how advanced and how convenient a programming language is. This also means that the language is capable enough not just to solve problems of the present, but of the future as well. A long line of features like scalability, being cloud-ready, and supporting newer paradigms and architectures is expected of a "modern" programming language.

Today, in fact, java is the most used runtime platform on enterprise systems (more than 97% of desktops). But more than that, its virtual machine powers packages and custom business applications, and a wide array of mobile and other embedded platforms.

Currently, according to Oracle, more than 3 billion devices run Java in some form. Most major companies use Java for some of their functions and Java server applications are processing tens of millions of requests each day.

Why java is so popular?

One of the most important reasons why Java is so popular is the platform independence. Java is a concurrent, class-based, and object-oriented programming language. It was initially designed to have as few implementation dependencies as possible, which lead to the term "write once, run anywhere" (WORA). This means that compiled Java code can run on all platforms with no need for recompiling the code.

Java-based applications are known for their speed and scalability. Its efficient processing speeds are used in software, computer games, and mobile Apps. Java supports Multithreading. Multithreading means handling more than one job at a time, so get more process get done in less time than it could with just one thread. Java is also a statically typed language, so that it brings a much greater degree of safety and stability to its programs compared to other popular languages. This safety and stability is a necessity for companies who require major bandwidth in their software and apps.

Is Java worth learning?

Java is still a relevant programming language that shows no sign of declining in popularity. Most developers choose it up as their initial programming language because it's reasonably simple to learn.

Since the language has an English-like syntax with minimum special characters, Java could be learned in a very short time span and used to build appropriate applications. It is part of a family of languages that are heavily influenced by C++ (as well as C#), thus learning Java offers vast benefits when learning these alternative two languages.

"Developing programs is a kind of making art, once you learn clearly and spend your time with full involvement; the creation of art became so easy and simple."

General Advantages of Using Java for Business Applications:

Programming with Java is incredibly common for banking and web applications. Compared to other programming languages, Java definitely stands out in terms of security functionality and environment. It comes with certain built-in security features such as:

Java apps are able to manage their own use by multiple users at the same time, creating threads for each use within the program itself, rather than having to run multiple copies of the programming in the same hardware. Each thread is tracked until the "work" is finished.

Java is so versatile and provides robust customized solutions for almost any type of business need. This "referred position" shows no signs of declining, especially now that Java 10 is on the horizon. It just keeps getting better.

Advanced Authentication and Access Control that allows incorporating a range of secure login mechanisms, along with creating the custom security policy and enforce a well-defined permission access policy to sensitive data.

Cryptography

Advanced Authentication and Access Control that allows incorporating a range of secure login mechanisms, along with creating the custom security policy and enforce a well-defined permission access policy to sensitive data.

Java apps are able to manage their own use by multiple users at the same time, creating threads for each use within the program itself, rather than having to run multiple copies of the programming in the same hardware. Each thread is tracked until the "work" is finished.

Java is so versatile and provides robust customized solutions for almost any type of business need. This "referred position" shows no signs of declining, especially now that Java 10 is on the horizon. It just keeps getting better.

Nowadays, Android is not just on tens of thousands of different phones and tablets. It’s on your wrist, in your living room, in your car, and as soon we start assigning IP addresses to inanimate objects, it is going to be pretty much everywhere around us. A lot of ground for even an experienced Android developer to cover!

Also there are over one million apps on Google Play, not counting Amazon AppStore or markets we are not generally interested in.

So, how can an independent developer create a successful app in this huge market with big players? I have no idea, I haven’t made a successful app! But, I have made a cute one, and I’d like to share my story with you.

Few Android Studio Tips, Tricks & Resources you should be familiar with, as an Android Developer

The reason of this article, is to list helpful tips, tricks, shortcuts and resources for Android Studio that will improve your overall efficiency and performance.

1.Material Colors theme for Android Logcat

To change Android Studio Logcat you need to go to:Preferences (Settings on Windows / Linux machines) → Editor → Colors & Fonts → Android Logcat and change the foreground color for every type of log.

2. Prevent Android Studio Logcat from clearing the log for the current application when it crashes. 

To do that you need to go to theAndroid Monitorpanel and chooseEdit filter configurationon the right side dropdown.

3. Apply a proper code style to your IDE (IntelliJ / Android Studio). 

Go toPreferences → Code Style → Javaand in aSchemedropdown you can choose your code style (or set up a new one).

2 styles which are especially worth to mention:

Square Java Code Styles with Android

Google Java Code Style.

4. Use split screen for increasing efficiency.

To turn this feature on, you need to right mouse click on the tab of your main screen and choose Split Vertically / Horizontally feature.

But to be as efficient as possible we need to set up a custom keyboard’s shortcut. To do that go to Preferences → Keymap and search for Split Vertically. Then open a context menu and click Add Keyboard Shortcut. 

It is shown on a gif below. You can define the shortcut for a horizontal split view as well.

5. Distraction Free Mode. 

You can enable it by going to: View → Enter Distraction Free Mode

6. Use Live Templates

you can use many already defined templates, like for Toasts or if conditions.

you can use your own custom templates. Here is a great reference article by Reto Meier. You can also refer to the IntelliJ IDEA documentation.

Learn XML And Java

Extensible markup language (XML) is a markup language that’s the foundation for the Android user interface (UI) and elements. Java is the back-end coding language of the Android platform, and it’s used with binding functionality with your XML layouts. Let’s take a look at a small example.

<?xml version="1.0" encoding="utf-8"?> <layout xmlns:android=http://schemas.android.com/apk/res/android"> <data> <variable name="user" type="com.example.User"/> </data> <LinearLayout android:orientation="vertical" android:layout_width="match_parent" android:layout_height="match_parent"> <TextView android:layout_width="wrap_content" android:layout_height="wrap_content" android:text="@{user.firstName}"/> <TextView android:layout_width="wrap_content" android:layout_height="wrap_content" android:text="@{user.lastName}"/> </LinearLayout> </layout>

This small layout has one data object defined as “User.” For developers used to binding elements in even web design, this should be intuitive for you. The data element is defined in Java, and then the object data is bound to the front-end layout element. In this example, we have a first and last name. The following Java code is an example of a User class used for the binding.

public class User { public final String firstName; public final String lastName; public User (String firstName, String lastName){ this.firstName = firstName; this.lastName = lastName; } }

Notice that the constructor takes a first and last name variable and binds the data to the public class properties. When the screen opens on the device, this data is then bound to the front-end elements.

Before you get started in Android Application development, you should be expert in both of these technologies. Understanding the fundamentals of XML formats is relatively easy, but Java is an object-oriented language and object-oriented programming (OOP) can be challenging if you’re not familiar with the concept. A newer language and framework, Kotlin, has been introduced to help advance Android app development. It can also be used with legacy Java apps as Kotlin co-operates with Java code.

The best way to learn Java is to dive into someone else’s code. GitHub is filled with open-source projects that help you understand Java and OOP concepts. The better your OOP skills are, the easier it will be to create a better coded, less buggy Android app.

Understand Memory Limitations

Mobile devices don’t have the high memory limitations that desktops have. In mobile programming, you must learn to code with smaller memory limitations in mind. If you carelessly use memory on an Android device, you run the risk of your app crashing due to “out of memory” errors.

To work around this, use services on the device sparingly and release memory resources when your app is hidden. You can release memory resources by overriding the onTrimMemory event although you should know that this isn’t a magic solution that allows you to ignore other performance optimization throughout your code. The following is a small sample of pseudocode that shows you how to override the event.

@Override public void onTrimMemory(int level) { if (level >= TRIM_MEMORY_MODERATE) {

//release resources here } }

Google also recommends that you avoid or at least reduce the use of bitmaps to lower the memory use when your app is running on the device. 

Don’t Use The Emulator For Real-World Testing

The Emulator in Android Studio is good for development and even debugging, but it doesn’t fully simulate a working Android device. You can consider basic environment features with the Emulator, but when it comes to testing, you should always use actual devices.

Of course, this is a huge investment for an Android developer, but it will make the entire development and testing life cycle much more efficient. It also ensures that you’re able to test your applications in various conditions as the user changes settings on the actual device. You can, however, use automated cloud solutions such as Amazon’s Testing platform.

Testing should never be taken lightly. It should be a major part when you design and deploy apps for your clients. Fewer bugs means happier users, and it can also mean a more successful app in Google Play. This doesn’t mean that you need to buy every device on the market to test your app on, but you should account for some of the more popular ones and the most common screen sizes.