The document discusses using the lpSolveAPI package in R to solve linear programming problems. It provides three examples:
1) A farmer's profit maximization problem is modeled and solved using functions from lpSolveAPI like make.lp(), add.constraint(), and solve().
2) A simple minimization problem is created and solved to illustrate setting up the objective function and constraints.
3) A more complex problem is modeled to demonstrate setting sparse matrices, integer/binary variables, and customizing variable and constraint names.
What Is Dynamic Programming? | Dynamic Programming Explained | Programming Fo...Simplilearn
This presentation on 'What Is Dynamic Programming?' will acquaint you with a clear understanding of how this programming paradigm works with the help of a real-life example. In this Dynamic Programming Tutorial, you will understand why recursion is not compatible and how you can solve the problems involved in recursion using DP. Finally, we will cover the dynamic programming implementation of the Fibonacci series program. So, let's get started!
The topics covered in this presentation are:
1. Introduction
2. Real-Life Example of Dynamic Programming
3. Introduction to Dynamic Programming
4. Dynamic Programming Interpretation of Fibonacci Series Program
5. How Does Dynamic Programming Work?
What Is Dynamic Programming?
In computer science, something is said to be efficient if it is quick and uses minimal memory. By storing the solutions to subproblems, we can quickly look them up if the same problem arises again. Because there is no need to recompute the solution, this saves a significant amount of calculation time. But hold on! Efficiency comprises both time and space difficulty. But, why does it matter if we reduce the time required to solve the problem only to increase the space required? This is why it is critical to realize that the ultimate goal of Dynamic Programming is to obtain considerably quicker calculation time at the price of a minor increase in space utilized. Dynamic programming is defined as an algorithmic paradigm that solves a given complex problem by breaking it into several sub-problems and storing the results of those sub-problems to avoid the computation of the same sub-problem over and over again.
What is Programming?
Programming is an act of designing, developing, deploying an executlable software solution to the given user-defined problem.
Programming involves the following stages.
- Problem Statement
- Algorithms and Flowcharts
- Coding the program
- Debug the program.
- Documention
- Maintainence
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S.
Learn more at: https://www.simplilearn.com/mobile-and-software-development/python-development-training
Linear programming
Application Of Linear Programming
Advantages Of L.P.
Limitation Of L.P.
Slack variables
Surplus variables
Artificial variables
Duality
Modeling and Solving Scheduling Problems with CP OptimizerPhilippe Laborie
This presentation focuses on using CP Optimizer to address scheduling problems. We will initially cover modeling concepts related with scheduling in CP Optimizer. Using examples we will then provide details on tools, functionalities and tips for speeding-up the development of your scheduling models and improving their efficiency.
Introduction to Dynamic Programming, Principle of OptimalityBhavin Darji
Introduction
Dynamic Programming
How Dynamic Programming reduces computation
Steps in Dynamic Programming
Dynamic Programming Properties
Principle of Optimality
Problem solving using Dynamic Programming
this is an presentation about Big O Notation. Big O notaion is very useful to check the limitation and effeciecy of an algorithm in its worst cases.in these slides the examples about O(1),O(n),O(n^2) and O(n!) with some example algorithms in C++.
What Is Dynamic Programming? | Dynamic Programming Explained | Programming Fo...Simplilearn
This presentation on 'What Is Dynamic Programming?' will acquaint you with a clear understanding of how this programming paradigm works with the help of a real-life example. In this Dynamic Programming Tutorial, you will understand why recursion is not compatible and how you can solve the problems involved in recursion using DP. Finally, we will cover the dynamic programming implementation of the Fibonacci series program. So, let's get started!
The topics covered in this presentation are:
1. Introduction
2. Real-Life Example of Dynamic Programming
3. Introduction to Dynamic Programming
4. Dynamic Programming Interpretation of Fibonacci Series Program
5. How Does Dynamic Programming Work?
What Is Dynamic Programming?
In computer science, something is said to be efficient if it is quick and uses minimal memory. By storing the solutions to subproblems, we can quickly look them up if the same problem arises again. Because there is no need to recompute the solution, this saves a significant amount of calculation time. But hold on! Efficiency comprises both time and space difficulty. But, why does it matter if we reduce the time required to solve the problem only to increase the space required? This is why it is critical to realize that the ultimate goal of Dynamic Programming is to obtain considerably quicker calculation time at the price of a minor increase in space utilized. Dynamic programming is defined as an algorithmic paradigm that solves a given complex problem by breaking it into several sub-problems and storing the results of those sub-problems to avoid the computation of the same sub-problem over and over again.
What is Programming?
Programming is an act of designing, developing, deploying an executlable software solution to the given user-defined problem.
Programming involves the following stages.
- Problem Statement
- Algorithms and Flowcharts
- Coding the program
- Debug the program.
- Documention
- Maintainence
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S.
Learn more at: https://www.simplilearn.com/mobile-and-software-development/python-development-training
Linear programming
Application Of Linear Programming
Advantages Of L.P.
Limitation Of L.P.
Slack variables
Surplus variables
Artificial variables
Duality
Modeling and Solving Scheduling Problems with CP OptimizerPhilippe Laborie
This presentation focuses on using CP Optimizer to address scheduling problems. We will initially cover modeling concepts related with scheduling in CP Optimizer. Using examples we will then provide details on tools, functionalities and tips for speeding-up the development of your scheduling models and improving their efficiency.
Introduction to Dynamic Programming, Principle of OptimalityBhavin Darji
Introduction
Dynamic Programming
How Dynamic Programming reduces computation
Steps in Dynamic Programming
Dynamic Programming Properties
Principle of Optimality
Problem solving using Dynamic Programming
this is an presentation about Big O Notation. Big O notaion is very useful to check the limitation and effeciecy of an algorithm in its worst cases.in these slides the examples about O(1),O(n),O(n^2) and O(n!) with some example algorithms in C++.
I am Josh U. I am a Python Homework Expert at pythonhomeworkhelp.com. I hold a Master's in Computer Science from, St. Edward’s University, USA. I have been helping students with their homework for the past 10 years. I solve homework related to Python.
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Computers and Programming , Programming Languages Types, Problem solving, Introduction to the MATLAB environment, Using MATLAB Documentation
Introduction to the course, Operating methodology-Installation Procedure
1. Compare a sample code in C with MATLAB
2. Trajectory of a particle in projectile motion ( solving quadratic equations)
3. Ideal gas law problem to find volume
Write an MPI program that implements a shell-sort like parallel algo.pdfbharatchawla141
What high-level position manager is responsible for working with the CEO and company
president to determine the company\'s competitive strategy?
Solution
Answer:
Responsibilities of a high-level position manager working with the CEO and company president
to determine company\'s competitive strategy are as below-
(1) To make available company\'s financial on time to CEO and company president.
(2) To make them aware about the employees\' performance and satisfaction level.
(3) To request for change in practices and policies of company for betterment of company.
(4) To make them aware about competitor\'s product, services and practices.
(5) To implement decision taken by CEO/President in the meeting.
(6) To present competitive analysis of market share, price of product, services offered by the
company.
(7) To answer all the queries asked by CEO/President in timely and correct manner.
(8) To give proper updation about competitors new strategy, their new policies and impact of
those on company.
(9) To actively participate in making company\'s strategy with CEO and company president.
(10) To ask questions raised by the employees/customers/suppliers etc. for company\'s product
and services..
Covid19py by Konstantinos Kamaropoulos
A tiny Python package for easy access to up-to-date Coronavirus (COVID-19, SARS-CoV-2) cases data.
ref:https://github.com/Kamaropoulos/COVID19Py
https://pypi.org/project/COVID19Py/?fbclid=IwAR0zFKe_1Y6Nm0ak1n0W1ucFZcVT4VBWEP4LOFHJP-DgoL32kx3JCCxkGLQ
"optrees" package in R and examples.(optrees:finds optimal trees in weighted ...Dr. Volkan OBAN
Finds optimal trees in weighted graphs. In
particular, this package provides solving tools for minimum cost spanning
tree problems, minimum cost arborescence problems, shortest path tree
problems and minimum cut tree problem.
by Volkan OBAN
k-means Clustering in Python
scikit-learn--Machine Learning in Python
from sklearn.cluster import KMeans
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.[wikipedia]
ref: http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html
Forecasting through ARIMA Modeling using R
ref:http://ucanalytics.com/blogs/step-by-step-graphic-guide-to-forecasting-through-arima-modeling-in-r-manufacturing-case-study-example/
k-means Clustering and Custergram with R.
K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no outcome to be predicted, and the algorithm just tries to find patterns in the data. In k means clustering, we have the specify the number of clusters we want the data to be grouped into. The algorithm randomly assigns each observation to a cluster, and finds the centroid of each cluster.
ref:https://www.r-bloggers.com/k-means-clustering-in-r/
ref:https://rpubs.com/FelipeRego/K-Means-Clustering
ref:https://www.r-bloggers.com/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/
Data Science and its Relationship to Big Data and Data-Driven Decision MakingDr. Volkan OBAN
Data Science and its Relationship to Big Data and Data-Driven Decision Making
To cite this article:
Foster Provost and Tom Fawcett. Big Data. February 2013, 1(1): 51-59. doi:10.1089/big.2013.1508.
Foster Provost and Tom Fawcett
Published in Volume: 1 Issue 1: February 13, 2013
ref:http://online.liebertpub.com/doi/full/10.1089/big.2013.1508
https://www.researchgate.net/publication/256439081_Data_Science_and_Its_Relationship_to_Big_Data_and_Data-Driven_Decision_Making
R Machine Learning packages( generally used)
prepared by Volkan OBAN
reference:
https://github.com/josephmisiti/awesome-machine-learning#r-general-purpose
Data visualization with R.
Mosaic plot .
---Ref: https://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture17.pdf
http://www.statmethods.net/advgraphs/mosaic.html
https://stat.ethz.ch/R-manual/R-devel/library/graphics/html/mosaicplot.html
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. Prepared by Volkan OBAN
LINEAR PROGRAMMING WİTH R— lpsolve and IpSolveAPI Package:
The lpSolveAPI package provides an R API for the lp solve library, a mixed integer linear pro
gramming (MILP) solver with support for pure linear, (mixed) integer/binary, semi-continuou
s and special ordered sets (SOS) models. The lp solve library uses the revised simplex method
to solve pure linear programs and uses the branch-and-bound algorithm to handle integer varia
bles, semi-continuous variables and special ordered sets.
Example:
maximize
P = (110)(1.30)x + (30)(2.00)y = 143x + 60y
subject to
120x + 210y <= 15000
110x + 30y <= 4000
x + y <= 75
x >= 0
y >= 0
Using R to solve
Install lpsolve library
3. R3 1 1 <= 75
Kind Std Std
Type Real Real
Upper Inf Inf
Lower 0 0
Solve
> solve(lprec)
[1] 0
Get maximum profit
> get.objective(lprec)
[1] 6315.625
Get the solution
> get.variables(lprec)
[1] 21.875 53.125
Thus, to achieve the maximum profit ($6315.625), the farmer
5. > solve(my.lp)
[1] 0
>
> get.objective(my.lp)
[1] -3.5
>
> get.variables(my.lp)
[1] 1.5 0.5
>
> get.constraints(my.lp)
[1] 3 2 3
Example:
First, we create an LPMO with 3 constraints and 4 decision variables.
> lprec <- make.lp(3, 4)
Next we set the values in the
first column. > set.column(lprec, 1, c(0, 0.24, 12.68))
The lp solve library is capable of using a sparse vectors to represent the constraints matrix. In
the remaining three columns, only the nonzero entries are set.
> set.column(lprec, 2, 78.26, indices = 1)
> set.column(lprec, 3, c(11.31, 0.08), indices = 2:3)
> set.column(lprec, 4, c(2.9, 0.9), indices = c(1, 3))
Next, we set the objective function, constraint types and right-hand-side.
> set.objfn(lprec, c(1, 3, 6.24, 0.1))
> set.constr.type(lprec, c(">=", "<=", ">="))
> set.rhs(lprec, c(92.3, 14.8, 4))
By default, all decision variables are created as real with range [0,∞). Thus we must change
the type of the decision variables x2 and x3.
6. > set.type(lprec, 2, "integer")
> set.type(lprec, 3, "binary")
We still need to set the range constraints on x1 and x4.
> set.bounds(lprec, lower = c(28.6, 18), columns = c(1, 4))
> set.bounds(lprec, upper = 48.98, columns = 4) Finally, we name the decision variables and
the constraints.
> RowNames <- c("THISROW", "THATROW", "LASTROW")
> ColNames <- c("COLONE", "COLTWO", "COLTHREE", "COLFOUR")
> dimnames(lprec) <- list(RowNames, ColNames)
Lets take a look at what we have done so far.
[1] 92.3000 6.8640 391.2928