Cost optimization problem where a manufacturing firm has entered into the contract to supply 50 refrigerators at the end of the first month, 50 at the end of the second month and 50 at the end of third. The cost of producing x refrigerators in any month is given by $ (x2 + 1000). The firm can produce more number of refrigerators and can carry them to subsequent month. It cost $20 per unit for any refrigerator to be carried from one month to the next one
Greedy with Task Scheduling Algorithm.pptRuchika Sinha
A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time
Python- Creating Dictionary,
Accessing and Modifying key: value Pairs in Dictionaries
Built-In Functions used on Dictionaries,
Dictionary Methods
Removing items from dictionary
Greedy with Task Scheduling Algorithm.pptRuchika Sinha
A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time
Python- Creating Dictionary,
Accessing and Modifying key: value Pairs in Dictionaries
Built-In Functions used on Dictionaries,
Dictionary Methods
Removing items from dictionary
Xmeasures - Accuracy evaluation of overlapping and multi-resolution clusterin...Artem Lutov
Performance of clustering algorithms is evaluated with the help of accuracy metrics. There is a great diversity of clustering algorithms, which are key components of many data analysis and exploration systems. However, there exist only few metrics for the accuracy measurement of overlapping and multi-resolution clustering algorithms on large datasets. In this paper, we first discuss existing metrics, how they satisfy a set of formal constraints, and how they can be applied to specific cases. Then, we propose several optimizations and extensions of these metrics. More specifically, we introduce a new indexing technique to reduce both the runtime and the memory complexity of the Mean F1 score evaluation. Our technique can be applied on large datasets and it is faster on a single CPU than state-of-the-art implementations running on high-performance servers. In addition, we propose several extensions of the discussed metrics to improve their effectiveness and satisfaction to formal constraints without affecting their efficiency. All the metrics discussed in this paper are implemented in C++ and are available for free as open-source packages that can be used either as stand-alone tools or as part of a benchmarking system to compare various clustering algorithms.
Lets learn a bit of involving inverse trigonometric functions. Hope you will enjoy learning it.Mainly this presentation is focused on finding inverse for trigonometric functions,we will learn right from functions to inverse functions and so the integration
Accelerating Random Forests in Scikit-LearnGilles Louppe
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include:
- An efficient formulation of the decision tree algorithm, tailored for Random Forests;
- Cythonization of the tree induction algorithm;
- CPU cache optimizations, through low-level organization of data into contiguous memory blocks;
- Efficient multi-threading through GIL-free routines;
- A dedicated sorting procedure, taking into account the properties of data;
- Shared pre-computations whenever critical.
Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.
I am Gill H. I am a Programming Assignment Expert at programminghomeworkhelp.com. I hold a Ph.D. in Electronics Engineering from, the University of Texas, USA. I have been helping students with their homework for the past 8 years. I solve assignments related to Programming.
Visit programminghomeworkhelp.com or email support@programminghomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Programming Assignments.
I am Kepha M. I am a Control System Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, McGill University, Canada. I have been helping students with their homework for the past 8 years. I solve assignments related to Control Systems.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Control System Assignments.
Xmeasures - Accuracy evaluation of overlapping and multi-resolution clusterin...Artem Lutov
Performance of clustering algorithms is evaluated with the help of accuracy metrics. There is a great diversity of clustering algorithms, which are key components of many data analysis and exploration systems. However, there exist only few metrics for the accuracy measurement of overlapping and multi-resolution clustering algorithms on large datasets. In this paper, we first discuss existing metrics, how they satisfy a set of formal constraints, and how they can be applied to specific cases. Then, we propose several optimizations and extensions of these metrics. More specifically, we introduce a new indexing technique to reduce both the runtime and the memory complexity of the Mean F1 score evaluation. Our technique can be applied on large datasets and it is faster on a single CPU than state-of-the-art implementations running on high-performance servers. In addition, we propose several extensions of the discussed metrics to improve their effectiveness and satisfaction to formal constraints without affecting their efficiency. All the metrics discussed in this paper are implemented in C++ and are available for free as open-source packages that can be used either as stand-alone tools or as part of a benchmarking system to compare various clustering algorithms.
Lets learn a bit of involving inverse trigonometric functions. Hope you will enjoy learning it.Mainly this presentation is focused on finding inverse for trigonometric functions,we will learn right from functions to inverse functions and so the integration
Accelerating Random Forests in Scikit-LearnGilles Louppe
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include:
- An efficient formulation of the decision tree algorithm, tailored for Random Forests;
- Cythonization of the tree induction algorithm;
- CPU cache optimizations, through low-level organization of data into contiguous memory blocks;
- Efficient multi-threading through GIL-free routines;
- A dedicated sorting procedure, taking into account the properties of data;
- Shared pre-computations whenever critical.
Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.
I am Gill H. I am a Programming Assignment Expert at programminghomeworkhelp.com. I hold a Ph.D. in Electronics Engineering from, the University of Texas, USA. I have been helping students with their homework for the past 8 years. I solve assignments related to Programming.
Visit programminghomeworkhelp.com or email support@programminghomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Programming Assignments.
I am Kepha M. I am a Control System Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, McGill University, Canada. I have been helping students with their homework for the past 8 years. I solve assignments related to Control Systems.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Control System Assignments.
How to optimize with the help of the Particle Swarm Optimization Technique and xlOptimizer ? This brief tutorial will enable you to solve any optimization problem with the application of Particle Swarm Optimization Method. After a brief introduction about the method the tutorial will show you the steps that you will need to follow for application of PSO in optimization even if you do not know any programming.(Some basic knowledge of MS Excel 2010 and later is required).
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Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
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Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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2. Problem Statement:
This is basically a cost optimization problem where a manufacturing firm has
entered into the contract to supply 50 refrigerators at the end of the first month,
50 at the end of the second month and 50 at the end of third. The cost of
producing x refrigerators in any month is given by $ (x2
+ 1000). The firm can
produce more number of refrigerators and can carry them to subsequent month.
It cost $20 per unit for any refrigerator to be carried from one month to the next
one.
Objective function:
Total Cost = Production Cost + Holding Cost
Let the number of refrigerators produced in first month = x1
Similarly the number produced in second month = x2
In third month = x3
Total cost = (x1
2
+ 1000) + (x2
2
+ 1000) + (x3
2
+ 1000) +20* (x1 - 50) + 20*(x1 + x2 -
100)
So the cost function becomes: x1
2
+ x2
2
+ x3
2
+ 40x1 + 20x2
Constraint Function:
x1 -50 > = 0
x1 + x2 -100 > = 0
x1 + x2 + x3 -150 >=0
Aim of the Project:
3. The above problem has been taken up from book on Engineering Optimization by
Dr S.S. Rao.
The problem has been solved using two methodologies
Classical Method
Kuhn Tucker Method
Non Classical method
Genetic Algorithm
Particle Swarm Algorithm
Differential Evolution Algorithm
The solution of the problem obtained using the Kuhn Tucker condition was
x1= 50; x2 = 50; x3 = 50
The main purpose of our project is to compare the Non Classical methods.
Genetic Algorithm
MATLAB optimization toolbox was used to get the optimum value objective
function. For the purpose two .m files were made one containing the fitness
function and the other containing the constraint equations. Optimization toolbox
was used with the default initial population of 50. Comparison results are
presented using various selection methods which were covered in lecture class.
4. The functional evaluation during different generations is also presented here:
The optimized value of the cost function obtained was 10504.8 after using GA
where the classical method gave the value equal to 10500. By running several trial
with different initial population sizes the value improved and the optimum value
was more closer to 10500.
Particle Swarm Algorithm
The algorithm works on the principle of personal best and global best approach
and tries to capture the behavior of flocking birds in search of food. The algorithm
was coded to satisfy the constraints by modifying the existing code provided by Dr
Rajib Bhattacharya (Course Instructor: Optimization Methods). The code is given
below as:
clear all;
close all;
for p = 1:4
tm = cputime;
Generation f(x) constraint
1 1851.1 0
2 13441.6 0
3 10427.4 0
4 10498.7 0
5 10504.4 0
6 10504.8 0
5. numPart = 5; % number of particles
numVar =3; % Number of variables
fileName = 'objfunc';
w = 0.5; % Inertia weight
C1 = 2; % learning factor for local search
C2 = 2; % learning factor for local search
maxGen =500; % Maximum generation
lb = 50; % Lower bound of the variables
ub = 180; % Upper bound of the variables
X = lb + (ub-lb)*rand(numPart,numVar); % initialize X
V = lb + (ub-lb)*rand(numPart,numVar); % initialize V
for i=1:numPart
% f(i)=fitness(X(i,:));
f(i)=feval(fileName,X(i,:));
end
X = [V X f'];
Y = sortrows(X,2*numVar+1);
pbest = Y;
gbest = Y(1,:);
for gen=1:maxGen % generation loop
for part=1:numPart % Particle loop
for dim=1:numVar % Variable loop
V(part,dim)=w*V(part,dim)+C1*rand(1,1)*(pbest(part,numVar+dim)-
X(part,numVar+dim))+C2*rand(1,1)*(gbest(numVar+dim)-X(part,numVar+dim));
X(part,numVar+dim)=X(part,numVar+dim)+V(part,dim);
end
while(X(part,numVar + 1)< 0 || X(part,numVar + 2)< 0 || X(part,numVar
+ 3)<0 || X(part,numVar + 1) - 50 <= 0 || X(part,numVar + 1) + X(part,numVar
+ 2)-100 <= 0 || X(part,numVar + 1) + X(part,numVar + 2) + X(part,numVar +
3) -150 <=0)
for dim=1:numVar % Variable loop
V(part,dim)=w*V(part,dim)+C1*rand(1,1)*(pbest(part,numVar+dim)-
X(part,numVar+dim))+C2*rand(1,1)*(gbest(numVar+dim)-X(part,numVar+dim));
X(part,numVar+dim)=X(part,numVar+dim)+V(part,dim);
end
end
%
% fnew = fitness(X(part,numVar+1:numVar+dim));
fnew = feval(fileName,X(part,numVar+1:numVar+dim));
X(part,2*numVar+1)=fnew;
if (fnew<X(part,2*numVar+1))
pbest(part,:)=X(part,:);
end
end
6. Y = sortrows(X,2*numVar+1);
if (Y(1,2*numVar+1)<gbest(2*numVar+1))
gbest=Y(1,:);
end
first_var(gen) = gbest(4);
second_var(gen) = gbest(5);
third_var(gen) = gbest(6);
obj_value(gen,p) = gbest(7);
disp(['Generation ', num2str(gen)]);
disp(['Best Value ', num2str(gbest(numVar+1:2*numVar+1))]);
end
numPart = numPart + 15;
end
generations = 1:500;
% subplot(2,2,1)
% plot(generations,obj_value(:,1))
% hold on
% subplot(2,2,2)
% plot(generations,obj_value(:,2))
% hold on
% subplot(2,2,3)
% plot(generations,obj_value(:,3))
% hold on
% subplot(2,2,4)
% plot(generations,obj_value(:,4))
plot(generations,obj_value(:,1),'b',generations,obj_value(:,2),'g',generation
s,obj_value(:,3),'k',generations,obj_value(:,4),'r')
cpu_time = cputime-tm ;
The modified part has been highlighted above and based on the above code some
of the results were plotted which are shown as:
7. The graph shows how the values are evolved after each generation the code is
run. The constraints are always taken care of because of the highlighted
condition. The above suggests that the value are converged after 300 generations
and this being the major difference between Swarm Optimization and GA where
the values were getting converged quickly after the 6th
generation itself.
8. This figure shows the effect of number of particles in swarm optimization. It is
very clear that as the number of particles increase the value of the objective
function converges to the closer optimum value thereby improving the efficiency
of the algorithm. However the computational time also increases by increasing the
number of particles. Still the value of the objective function obtained using GA was
much better than the Swarm Optimization in this particular study.
The combined behavior can be seen as:
9. Differential Evolution Algorithm
The problem was solved using MS Excel and the results were obtained as:
X1 = 50.06253
X2 = 49.94802
X3 = 49.99007
Function value = 10524.4
Precision = 0.00001
However the important thing noted while solving the differential evolution
algorithm was that it took a lot of time for the algorithm to converge.
This completes the brief comparative study on variety of algorithms. To
summarize the discussions we can list few observations:
• In PSO the optimal solutions have converged after 300 generations & no. of
Particles = 50 whereas in Genetic Algorithm solutions converge after 6
iterations.
• In PSO greater is the particles no. greater is the precision obtained.
• As GA is inbuilt tool box it takes more time.