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Joey D.Civil Engineer with Photography and Editing Skills, Philippines
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Whenever there is a discussion regarding storing information on a 3rd party's database system, questions on security follow. Entrusting another company to stage your valuable information safe is a massive step. Once that information is in your control, you are aware of the protection measures in place to keep it safe.

 

Google assures users that it keeps all information safe and personal unless the user chooses to share files with others. As a part of its security measures, Google does not discuss its approach to security very well. Since users should have a Google account to access Google Docs, and since all accounts need passwords, we all know that at least one stage in Google's security plan depends on password protection.

 

Google Docs is the free data processing software that comes with a Google account. It’s designed to be easy to use. It can be used to create documents with rich formatting, images, and tables and features like footnotes, headers and footers, and page numbering. You can create your documents more engaging with pictures, drawing objects, and tables in Google docs.

 

Why Google Docs is the best way to create blog

If you're a professional blogger, all that you write must obviously be a result of your thorough research and will basically involve hard work. Whether it's Blogspot or WordPress, text editors of each of those blogging platforms are up to notch. Each text editors not only automatically save the post you are writing but also provide sufficient resources for content data formatting that helps you present well your content. Google Docs offers you the easiest and simplest way to format your content, provide blog templates, share it with collaborators, and even upload immediately to whichever CMS you use.

 

Integrate google keeps with google docs

Google Keep has officially been labelled as a part of the Google Suite of tools. It’s currently very easy to keep notes for a document you're working on. Along with the Explore feature, Google Docs has become a seriously impressive tool for business, education, and just about the other purpose that requires note keeping as you write. Google docs provide a tool to integrate google keep notes into document.

 

Migrate google docs to Microsoft word

Google Docs are in a web format, we can’t simply import them into Word! To open Google Docs in Microsoft Word, we need to need to convert Google Docs to Word’s DOCX format, then transfer it afterward. You can easily perform this conversion from Google Docs.

 

Google Docs has been around for a little while now. Businesses are adopting the tool as the way to extend efficiency and usability of information. I have yet to work for a business that actively uses Google Docs on a day to day, however I will definitely see the benefits of google docs.

  1. Accessibility: With Google Docs, staff can access the information 24/7 where they have an internet connection. This kind of flexibility is very useful, particularly for workers who are typically travelling and working from mobile devices.
  2. Version Control: Collaboration have a lot of importance within the workplace. Being able to not only access information from anyplace, but to be able to control the version of any document your staff are working on is a huge asset to your company. Google Docs permits you to add and take away collaborators. You can control exactly who can make changes to the document. In addition, multiple users can access and edit the same document at the same time.
  3. Easy to Learn: Google Docs is very straightforward and easy to pick up. If you have any experience with a word processor or programs such as Word, Excel, etc.
  4. Import/Export Flexibility: Google Docs imports and exports most file types, giving you the flexibility, you need when sending and receiving files from colleagues.

 

Hire Google Docs experts on Toogit.

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.

 

 

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