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Vikash K.Experienced Hadoop & AWS administrator., United States
$40 /hr
11 Years Exp.
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Certified AWS solution architect with many years of hadoop admin and dev ops experience.
Deepak Kumar Workday HCM consultant with time management, India
$10 /hr
7 Years Exp.
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I am a workday HCM consultant. I have around 7years of experience. Earlier i was handling SAP HCM and now I integrated to Workday our whole HR system....Read More
Datalamp T.Big Data Company, India
$20 /hr
4 Years Exp.
0 Followers
DataLamp A Big Data company, works on data-pipeline & visualization solutions. Expertise- Java: Core, Collection, Swing, Reflection, Thread...Read More
Balakrishna J.Bigdata Developer, India
$11 /hr
1 Years Exp.
0 Followers
I have 1.6 years of experience in Bigdata Development. I had done two POC, most of the time I have analysed varies kinds of data with different storag...Read More
Vinayak A.Free Lancer Design Engineer, India
/hr
2 Years Exp.
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I am a certified PCB Designer and experienced in arduino DIY projects. Also, experienced in PLC programming in codesys. I have graduated in Electrical...Read More
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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.

Are you planing to hire a CSS developer—how can you find a top developer for your front-end or PSD to CSS project?

 

CSS has been in use for more than 20 years and has become an integral part of any front-end development. Therefore, there is no shortage of developers with CSS listed on their resumes. Locating CSS developers is fairly easy, but choosing the perfect one is that much more of a challenge. This article offers a sampling of effective questions to help you identify the best CSS developers who are experts in their field.

 

What is CSS?

CSS stands for Cascading Style Sheets, CSS is a programming language that describes the style of a HTML document. If you want to customize your website background image, text colors or border then you need CSS design. Alongside HTML (responsible for structure) and JavaScript (responsible for interactivity), CSS (responsible for style) is one of the big three core components of the web.

 

Next, we learn about what a CSS developer does, and provide you with a general framework for writing a CSS project description to help you find the right developer for your requirements. How to hire a top css developer to get work done.

 

What kind of work a CSS developer should deliver to you? A front-end developer uses a combination of HTML, CSS, and JavaScript to build everything a user sees and interacts with on a website—everything from front-end features like fonts and sliders, to the overall manner in which web content like photos, videos, and articles are displayed in your web browser. A CSS developer who specializes in CSS, taking .psd files and mockups and writing the CSS code that incorporates all of the colors, padding, margins, and more that comprise those designs. Beyond the fundamentals, they can work magic with raw CSS, are well versed in preprocessors like LESS/Sass, and may even use a front-end CSS framework like Bootstrap or Foundation.

 

Hire the best CSS Developers Work with the world's best talent on Toogit — the top growing freelancing website trusted by over 150,000 users.

 

Writing A CSS Development Project Description

After you get a firm idea of your project deliverables, it’s time to write a project description. The way you write a description will determine the quality of developer that you’ll attract. It’s important to be concise yet detailed enough so developers interested in your project can submit proposals with fairly accurate cost and time estimates. Here our recommendation to use Toogit’s auto-proposal to speed up your hiring procedure and feel the power of AI in freelancing.

 

The title of your project description can include the type of development that you need. You know that you need a CSS developer, but why specifically a front-end developer specialized in CSS? The title should attract CSS developers with the specific technologies or skills you require for your project.

 

Next is the project overview. Describe what you’re planning to build or what you’ll need the developer to do. Be as detailed as possible, and include any wireframes or mockups that can help you attract the talented developer for your needs. 

 

Part of your description should also define the deliverables including any designs, documentation, or source code. 

 

Sample CSS Project Description

Below sample will help you to write a perfect project description. 

 

Project Title:

CSS Developer for a Fashion design website 

 

Description: 

We’re looking for an expert CSS developer to help us build an exciting new fashion design website template. The project is based on the (MongoDB, AngularJS, and Node.js) stack, so familiarity using Bootstrap with AngularJS is required.

The right developer will be able to provide us with the following skills and services:

  • Translation of designer mock-ups and wireframes into front-end code
  • Front-end integration with a MEAN back-end
  • Unit testing
  • Bootstrap, LESS, AngularJS
  • Familiarity with API Creation and RESTful services

 

Project Scope & Deliverables:

While much of the project has already been completed, we still need additional support to help us polish our website and meet our launch deadline in 4 months (mm/dd/yyyy). We will need the following three deliverables:

Deliverable #1 by (date) 

Deliverable #2 by (date) 

Deliverable #3 by (date)

Hire a CSS Designer

On Toogit.com you can hire CSS coders and designers to make your web design and custom CSS project shine. Get started today.

 

Conclusion

For a top CSS developer, read our css interview question and answer section this might come off as a bit basic. However, It cover most of the core CSS concepts and principles, and provide a starting point for evaluating individuals. Being able to discuss CSS principles and concepts in a clear and coherent manner will demonstrate candidate’s communication skills as well as their theoretical and peripheral subject knowledge. Finding true CSS expert is a challenge. We hope you find the interview questions to be a useful foundation in your quest for the elite few among CSS developers. Finding such candidates is well worth the effort, as they will undoubtedly have a significant positive impact on your team’s productivity and results.

Python is one of the fastest growing programming languages. It has undergone more than 28 years of the successful span. Python itself reveals its success story and a promising future ahead. Python programming language is presently being used by a number of high traffic websites including Google, Yahoo Groups, Yahoo Maps, Shopzilla, Web Therapy, Facebook, NASA, Nokia, IBM, SGI Inc, Quora, Dropbox, Instagram and Youtube. Similarly, Python also discovers a countless use for creating gaming, financial, scientific and instructive applications.

 

Python is a fast, flexible, and powerful programing language that's freely available and used in many application domains. Python is known for its clear syntax, concise code, fast process, and cross-platform compatibility.

 

Python is considered to be in the first place in the list of all AI and machine learning development languages due to the simplicity. The syntaxes belonging to python are terribly easy and can be easily learn. Therefore, several AI algorithms will be easily implemented in it. Python takes short development time as compared to different languages like Java, C++ or Ruby. Python supports object oriented, functional as well as procedure oriented styles of programming. There are lots of libraries in python that make our tasks easier.

 

Some technologies relying on python:

Python has become the core language as far as the success of following technologies is concerned. Let’s dive into the technologies which use python as a core element for research, production and further developments.

 

  1. Networking: Networking is another field in which python has a brighter scope in the future. Python programming language is used to read, write and configure routers and switches and perform other networking automation tasks in a cost-effective and secure manner.
  2. Big Data: The future scope of python programming language can also be predicted by the way it has helped big data technology to grow. Python has been successfully contributing in analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries.
  3. Artificial Intelligence (AI): There are plenty of python frameworks, libraries, and tools that are specifically developed to direct Artificial Intelligence to reduce human efforts with increased accuracy and efficiency for various development purposes. It is only the Artificial Intelligence that has made it possible to develop speech recognition system, interpreting data like images, videos etc.

 

Why Choose Python for Artificial Intelligence and Machine Learning?

Whether a startup or associate MNC, Python provides a large list of benefits to all. The usage of Python is specified it cannot be restricted to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complicated processes like artificial intelligence (AI), Machine Learning (ML), natural language process, data science etc. The question is why Python is gaining such momentum in AI? And therefore the answer lies below:

 

Flexibility: Flexibility is one of the core advantages of Python. With the option to choose between OOPs approach and scripting, Python is suitable for every purpose. It works as a perfect backend and it also suitable for linking different data structures together.

 

Platform agnostic: Python provides developer with the flexibility to provide an API from the existing programming language. Python is also platform independent, with just minor changes in the source codes, you can get your project or application up and running on different operating systems.

 

Support: Python is a completely open source with a great community. There is a host of resources available which can get any developer up to speed in no time. Not to forget, there is a huge community of active coders willing to help programmers in every stage of developing cycle.

 

Prebuilt Libraries: Python has a lot of libraries for every need of your AI project. Few names include Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning.

 

Less Code: Python provides ease of testing - one of the best among competitors. Python helps in easy writing and execution of codes. Python can implement the same logic with as much as 1/5th code as compared to other OOPs languages.

 

Applications of Python:

There are so many applications of Python in the real world. But over time we’ve seen that there are three main applications for Python

Web Development: Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.

Data Science (including Machine Learning): Machine Learning with Python has made it possible to recognize images, videos, speech recognition and much more.

Data Analysis/Visualization: Python is also better for data manipulation and repeated tasks. Python helps in the analysis of a large amount of data through its high-performance libraries and tools. One of the most popular Python libraries for the data visualization is Matplotlib.

 

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