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Kingkarn Chubamrung Transaction Processing Analyst, China
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My name is Kingkarn Chubamrung. You can call me Nairne. I hold a Bachelor's Degree in Thai Language and Literature as well as Bachelor’s Degree i...Read More
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Kind words are worth much and cost little. This creates opportunity: when you can’t out-spend the competition, the solution is to out-support them.

 

Here are 14 tips from the support documentation of world leaders in providing best customer service. I hope these will help you in improving your customer service.

 

  1. Practice sympathy, Patience, and Consistency: Some of your customers will be full of queries, some just chatty, and others plain mad. You must be ready to handle all of them and provide the same level of service every time.
  2. Good customer Service may be a Continuous Learning process: Every customer is unique and each support situation is totally different. In order to handle surprises, sense a customer’s mood, address new challenges consequently, you've got to be willing to stay learning. Strive to have a deep understanding of your customer’s challenges and still search for better ways to deal with them.
  3. Train customer service agent for understanding product detail: The training and development program must cover all aspects of how to deal with issues, talk to customers, and resolve problems.  Apart from that, your team should believe in and understand your product before they are sent out with the task of pacifying a customer. If your team does not know the product or the service well enough, then they will not have enough knowledge to tackle an issue.
  4. Solving a problem as soon as possible: When customers complain, your team must acknowledge the issue quickly and try to resolve it timely.
  5. Use technology to assist them: Customer support can only be performed accurately if we mix the technology with the humanistic approach.
  6. Add live chat to your website: Live chat can help your customer support team to interact with the customer in depth so that the customer can get resolved his queries easily.
  7. Use customer service templates: It’s important to keep your standards high and your response times low. Don’t waste keyboard strokes for every basic and common question. Build professional templates so you can respond to customer with ease and professionalism.
  8. Maintain a positive attitude: It is very important that your team keep a positive and bright attitude when managing your customers. If you maintain an accommodating and inviting attitude, then you will easily avoid conflicts.
  9. Apologize for any inconvenience: Whatever the issue, and whatever role your company played in the issue, you need to apologize to the customer. If the consumer’s credit card did not work, tell them you’re sorry for their inconvenience or that it happened.
  10. Listen to your customer: One of the simplest ways in which to serve your customer is by listening to them from the beginning. Most customers contact or reach bent on your support team to when they want to convey a message or a problem. Therefore, the first and most important thing you can do is to hear them out completely.
  11. Never say “I don’t know”: When your team represents your business, they need to speak as if the business itself is speaking. So, once an executive says “no”, Customer instantly diminishes the value of your business. “No” isn't an answer. If a customer is facing a difficulty, then your team should try and resolve it, one way or another.
  12. Admit your mistakes: If you mess up, admit it, even if you discover your mistakes before your customers do. Admitting you messed up builds trust and restores your customer’s confidence in your service. It also allows you to control the situation, re-focus the customer’s attention, and fix the problem.
  13. Follow up after a problem is solved: Follow up with your customers to ensure their issues were resolved properly and that they were satisfied with the service. Give them a call, send them an email etc.
  14. Always close conversations correctly: Every conversation you close with a customer should end with you saying “Is there anything else I can do for you today? I’m happy to help!”

 

Everyone should be feeling the customer’s pain points. When your whole company is encouraged to be involved in customer service, knowledge of problems, bugs, and features becomes illuminated for the entire team. There’s no faster way to make improvements that drive your business forward.

Welcome to Python programming world! We presume you are trying to find information concerning why and how to get started with Python. Fortunately, an experienced coder in any programing language (whatever it's going to be) will pick up Python very quickly. It is also easy for beginners to learn and use.

 

Why you should learn Python

Python is one of the most popular general-purpose programming languages used for both large and small-scale applications. With Python, you can discover how to bridge web development and data analytics. Python’s widespread adoption is due to its large standard library, easy readability, and support of multiple paradigms such as functional, procedural and object-oriented programming styles. Python modules interact with a variety of databases, making it an excellent choice for large-scale data analysis. The Python programming language is often the best choice for introductory courses in data science and machine learning. If you've been wondering how to learn python online to advance your career, you've come to the right destination.

 

A popular Python slogan “life is happier without braces”.

 

Install Python

Installing Python is generally easy, and today several Linux and UNIX system distributions include a recent Python. Even some Windows computers currently go along with Python already installed. If you do need to install Python download from Python official website.

 

Learning Python

Before getting started, you may want to find out which IDEs and text editor are best, IDE usually has plenty of useful features such as autocomplete, debugger and refactoring tools. Some will even check your Python code for little mistakes and encourage best practices through warnings. IDE will help you to find bugs and develop code faster. Learn basics of Python programming and syntax from online Python tutorials.

 

What you need to learn

  1. Python Syntax
  2. String and Console output
  3. Conditionals and control flow
  4. Function
  5. List and Dictionaries
  6. Loops and array
  7. Classes
  8. File input and output
  9. Advanced topic in python

 

Here are some tips to help you make the new concepts you are learning as a beginner programmer:

  1. Code Everyday: Consistency is very important when you are learning a new language. We recommend making a commitment to code every day.
  2. Write it out: As you progress on your journey as a new programmer, you may wonder if you should be taking notes. This will be especially beneficial for those working towards the goal of becoming a full-time developer, as many interviews will involve writing code on a whiteboard.
  3. Go Interactive: Whether you are learning about basic Python data structures (strings, lists, dictionaries, etc.) for the first time, or you are debugging an application, the interactive Python shell will be one of your best learning tools.
  4. Become bug hunter: Once you begin writing complicated programs that you just can run into bugs in your code. It happens to all or any of us! Don’t let bugs frustrate you. Instead, embrace these moments proudly and consider yourself as a bug bounty hunter.
  5. Surround yourself with others: It is extremely important when you are learning to code in Python that you simply surround yourself with others who are learning additionally. This may allow you to share the information and tricks you learn on the approach.
  6. Teach: It is said that the most effective way to learn something is to teach it. This is often true once you are learning Python. There are many ways to try to do this: white boarding with other Python lovers, writing blog posts explaining recently learned ideas, recording videos during which you explain something you learned, or simply talking to yourself at your computer.
  7. Pair program: Pair programming is a technique that involves two developers working to complete a task. The two developers switch between them. One developer writes the code, while other helps guide the problem solving and reviews the code as it is written. Switch frequently to get the benefit of both sides.
  8. Build something: For beginners, there are many small exercises that will really help you become confident with Python.

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.

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