The greedy choice at each step is to select the talk that ends earliest among the compatible options. This maximizes the chance of fitting additional talks into the schedule.
This document discusses algorithms and their properties. It begins by defining an algorithm as a finite set of precise instructions to perform a computation or solve a problem. It provides examples of algorithms like directions to a location or a recipe. The document then discusses how some algorithms are easier than others and provides examples. It also outlines key properties of algorithms like inputs, outputs, definiteness, and effectiveness. Later sections summarize various algorithms for tasks like searching, sorting, and finding maximum/minimum elements with analysis of their running times.
This document provides an overview of algorithms, including definitions and examples. It discusses different types of algorithms like searching and sorting algorithms. For searching, it describes linear search and binary search algorithms. It also analyzes the running time of these algorithms. For sorting, it introduces bubble sort and insertion sort algorithms and analyzes their running times, which are both about O(n^2) time. Faster sorting algorithms like heap sort, quick sort, and merge sort have running times of O(nlogn).
1. An algorithm is a precise set of instructions to perform a computation or solve a problem. Some algorithms are easier than others, such as finding the maximum value in a list, while others are harder like finding the shortest path between two locations.
2. The document describes several common algorithms including ones for finding the maximum value, doing linear and binary searches of a list, and sorting lists with bubble sort and insertion sort.
3. The running time of algorithms, measured by the number of steps needed in the worst case, varies from linear time (n steps) for linear search to logarithmic time (log n steps) for binary search to quadratic time (n^2 steps) for bubble and insertion sorts.
1. The document discusses several algorithms including those for finding the maximum element in a list, linear search, binary search, bubble sort, and insertion sort.
2. It provides pseudocode to describe each algorithm and analyzes their running times, finding that linear search takes n steps, binary search takes log2n steps, and bubble sort and insertion sort each take about n2 steps.
3. Faster sorting algorithms like heap sort, quick sort, and merge sort have running times of nlogn steps.
The document discusses two searching algorithms - linear search and binary search. Linear search sequentially compares the target element to each element in the array, while binary search uses a divide and conquer approach to quickly hone in on the target element in a sorted array. Both algorithms are demonstrated with pseudocode and examples.
The document discusses various searching, sorting, and hashing techniques used in data structures and algorithms. It describes linear and binary search methods for finding elements in a list or array. It also explains bubble, insertion, selection, and shell sort algorithms for arranging elements in ascending or descending order. Finally, it covers hashing techniques like hash functions, separate chaining, and open addressing that are used to map keys to values in a hash table.
The document discusses various searching and sorting algorithms including linear/sequential search, binary search, selection sort, bubble sort, insertion sort, quick sort, and merge sort. It provides descriptions of each algorithm and examples to illustrate how they work on sample data sets. Key steps and properties of each algorithm are outlined such as complexity, how elements are compared and swapped during sorting, and dividing arrays during searching.
1. The document discusses various data structures concepts including arrays, dynamic arrays, operations on arrays like traversing, insertion, deletion, sorting, and searching.
2. It provides examples of declaring and initializing arrays, as well as dynamic array allocation using pointers and new/delete operators.
3. Searching techniques like linear search and binary search are explained, with linear search comparing each element sequentially while binary search eliminates half the elements at each step for sorted arrays.
The document discusses various searching, sorting, and hashing techniques used in data structures and algorithms. It describes linear and binary search methods for finding elements in a list. Linear search has O(n) time complexity while binary search has O(log n) time complexity. Bubble, insertion, and selection sorts are covered as sorting algorithms that arrange elements in ascending or descending order, with bubble sort having O(n^2) time complexity. Hashing techniques like hash functions, separate chaining, and open addressing are also summarized at a high level.
This document provides information on various searching and sorting algorithms, including linear search, binary search, bubble sort, selection sort, and insertion sort. It begins with an overview of searching and describes linear and binary search algorithms. For linear search, it provides pseudocode and an example. For binary search, it also provides pseudocode and an example. The document then discusses sorting algorithms like bubble sort, selection sort, and insertion sort, providing descriptions, pseudocode, examples, and analyses of each.
The document discusses various searching and sorting algorithms such as linear search, binary search, selection sort, bubble sort, and insertion sort. It provides descriptions of how each algorithm works and includes pseudocode examples to illustrate the sorting process step-by-step. Linear search compares elements one by one until the target is found or all elements have been checked. Binary search compares the target to the middle element to determine which half of the list to search next. Selection sort iterates to find the minimum element and swap it into place at each step.
The document discusses various searching and sorting algorithms. It describes linear search, binary search, selection sort, bubble sort, and heapsort. For each algorithm, it provides pseudocode examples and analyzes their performance in terms of number of comparisons required in the worst case. Linear search requires N comparisons in the worst case, while binary search requires log N comparisons. Selection sort and bubble sort both require approximately N^2 comparisons, while heapsort requires 1.5NlogN comparisons.
The document discusses linear and binary search algorithms.
Linear search is the simplest algorithm that sequentially compares each element of an array to the target element. It has a worst case time complexity of O(N).
Binary search works on a sorted array by comparing the middle element to the target. It eliminates half of the remaining elements with each comparison. It has a worst case time complexity of O(log n), which is faster than linear search for large data sets.
Pseudocode and C programs are provided as examples to implement linear and binary search.
BASIC ALGORITHMS
SEARCHING (LINEAR SEARCH, BINARY SEARCH ETC.), BASIC SORTING ALGORITHMS (BUBBLE, INSERTION AND SELECTION), FINDING ROOTS OF EQUATIONS, NOTION OF ORDER OF COMPLEXITY THROUGH EXAMPLE PROGRAMS (NO FORMAL DEFINITION REQUIRED)
Construction Materials (Paints) in Civil EngineeringLavish Kashyap
This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
It will be very useful for all Civil Engineering students who wants to search about various Construction Materials used in Civil Engineering field.
Paint is a vital construction material used for protecting surfaces and enhancing the aesthetic appeal of buildings and structures. It consists of several components, including pigments (for color), binders (to hold the pigment together), solvents or thinners (to adjust viscosity), and additives (to improve properties like durability and drying time).
Paint is one of the material used in Civil Engineering field. It is especially used in final stages of construction project.
Paint plays a dual role in construction: it protects building materials and contributes to the overall appearance and ambiance of a space.
Ad
More Related Content
Similar to Lecture of algorithms and problem solving (20)
The document discusses various searching, sorting, and hashing techniques used in data structures and algorithms. It describes linear and binary search methods for finding elements in a list or array. It also explains bubble, insertion, selection, and shell sort algorithms for arranging elements in ascending or descending order. Finally, it covers hashing techniques like hash functions, separate chaining, and open addressing that are used to map keys to values in a hash table.
The document discusses various searching and sorting algorithms including linear/sequential search, binary search, selection sort, bubble sort, insertion sort, quick sort, and merge sort. It provides descriptions of each algorithm and examples to illustrate how they work on sample data sets. Key steps and properties of each algorithm are outlined such as complexity, how elements are compared and swapped during sorting, and dividing arrays during searching.
1. The document discusses various data structures concepts including arrays, dynamic arrays, operations on arrays like traversing, insertion, deletion, sorting, and searching.
2. It provides examples of declaring and initializing arrays, as well as dynamic array allocation using pointers and new/delete operators.
3. Searching techniques like linear search and binary search are explained, with linear search comparing each element sequentially while binary search eliminates half the elements at each step for sorted arrays.
The document discusses various searching, sorting, and hashing techniques used in data structures and algorithms. It describes linear and binary search methods for finding elements in a list. Linear search has O(n) time complexity while binary search has O(log n) time complexity. Bubble, insertion, and selection sorts are covered as sorting algorithms that arrange elements in ascending or descending order, with bubble sort having O(n^2) time complexity. Hashing techniques like hash functions, separate chaining, and open addressing are also summarized at a high level.
This document provides information on various searching and sorting algorithms, including linear search, binary search, bubble sort, selection sort, and insertion sort. It begins with an overview of searching and describes linear and binary search algorithms. For linear search, it provides pseudocode and an example. For binary search, it also provides pseudocode and an example. The document then discusses sorting algorithms like bubble sort, selection sort, and insertion sort, providing descriptions, pseudocode, examples, and analyses of each.
The document discusses various searching and sorting algorithms such as linear search, binary search, selection sort, bubble sort, and insertion sort. It provides descriptions of how each algorithm works and includes pseudocode examples to illustrate the sorting process step-by-step. Linear search compares elements one by one until the target is found or all elements have been checked. Binary search compares the target to the middle element to determine which half of the list to search next. Selection sort iterates to find the minimum element and swap it into place at each step.
The document discusses various searching and sorting algorithms. It describes linear search, binary search, selection sort, bubble sort, and heapsort. For each algorithm, it provides pseudocode examples and analyzes their performance in terms of number of comparisons required in the worst case. Linear search requires N comparisons in the worst case, while binary search requires log N comparisons. Selection sort and bubble sort both require approximately N^2 comparisons, while heapsort requires 1.5NlogN comparisons.
The document discusses linear and binary search algorithms.
Linear search is the simplest algorithm that sequentially compares each element of an array to the target element. It has a worst case time complexity of O(N).
Binary search works on a sorted array by comparing the middle element to the target. It eliminates half of the remaining elements with each comparison. It has a worst case time complexity of O(log n), which is faster than linear search for large data sets.
Pseudocode and C programs are provided as examples to implement linear and binary search.
BASIC ALGORITHMS
SEARCHING (LINEAR SEARCH, BINARY SEARCH ETC.), BASIC SORTING ALGORITHMS (BUBBLE, INSERTION AND SELECTION), FINDING ROOTS OF EQUATIONS, NOTION OF ORDER OF COMPLEXITY THROUGH EXAMPLE PROGRAMS (NO FORMAL DEFINITION REQUIRED)
Construction Materials (Paints) in Civil EngineeringLavish Kashyap
This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
It will be very useful for all Civil Engineering students who wants to search about various Construction Materials used in Civil Engineering field.
Paint is a vital construction material used for protecting surfaces and enhancing the aesthetic appeal of buildings and structures. It consists of several components, including pigments (for color), binders (to hold the pigment together), solvents or thinners (to adjust viscosity), and additives (to improve properties like durability and drying time).
Paint is one of the material used in Civil Engineering field. It is especially used in final stages of construction project.
Paint plays a dual role in construction: it protects building materials and contributes to the overall appearance and ambiance of a space.
The main purpose of the current study was to formulate an empirical expression for predicting the axial compression capacity and axial strain of concrete-filled plastic tubular specimens (CFPT) using the artificial neural network (ANN). A total of seventy-two experimental test data of CFPT and unconfined concrete were used for training, testing, and validating the ANN models. The ANN axial strength and strain predictions were compared with the experimental data and predictions from several existing strength models for fiber-reinforced polymer (FRP)-confined concrete. Five statistical indices were used to determine the performance of all models considered in the present study. The statistical evaluation showed that the ANN model was more effective and precise than the other models in predicting the compressive strength, with 2.8% AA error, and strain at peak stress, with 6.58% AA error, of concrete-filled plastic tube tested under axial compression load. Similar lower values were obtained for the NRMSE index.
Jacob Murphy Australia - Excels In Optimizing Software ApplicationsJacob Murphy Australia
In the world of technology, Jacob Murphy Australia stands out as a Junior Software Engineer with a passion for innovation. Holding a Bachelor of Science in Computer Science from Columbia University, Jacob's forte lies in software engineering and object-oriented programming. As a Freelance Software Engineer, he excels in optimizing software applications to deliver exceptional user experiences and operational efficiency. Jacob thrives in collaborative environments, actively engaging in design and code reviews to ensure top-notch solutions. With a diverse skill set encompassing Java, C++, Python, and Agile methodologies, Jacob is poised to be a valuable asset to any software development team.
DeFAIMint | 🤖Mint to DeFAI. Vibe Trading as NFTKyohei Ito
DeFAI Mint: Vive Trading as NFT.
Welcome to the future of crypto investing — radically simplified.
"DeFAI Mint" is a new frontier in the intersection of DeFi and AI.
At its core lies a simple idea: what if _minting one NFT_ could replace everything else? No tokens to pick.
No dashboards to manage. No wallets to configure.
Just one action — mint — and your belief becomes an AI-powered investing agent.
---
In a market where over 140,000 tokens launch daily, and only experts can keep up with the volatility.
DeFAI Mint offers a new paradigm: "Vibe Trading".
You don’t need technical knowledge.
You don’t need strategy.
You just need conviction.
Each DeFAI NFT carries a belief — political, philosophical, or protocol-based.
When you mint, your NFT becomes a fully autonomous AI agent:
- It owns its own wallet
- It signs and sends transactions
- It trades across chains, aligned with your chosen thesis
This is "belief-driven automation". Built to be safe. Built to be effortless.
- Your trade budget is fixed at mint
- Every NFT wallet is isolated — no exposure beyond your mint
- Login with Twitter — no crypto wallet needed
- No \$SOL required — minting is seamless
- Fully autonomous, fully on-chain execution
---
Under the hood, DeFAI Mint runs on "Solana’s native execution layer", not just as an app — but as a system-level innovation:
- "Metaplex Execute" empowers NFTs to act as wallets
- "Solana Agent Kit v2" turns them into full-spectrum actors
- Data and strategies are stored on distributed storage (Walrus)
Other chains can try to replicate this.
Only Solana makes it _natural_.
That’s why DeFAI Mint isn’t portable — it’s Solana-native by design.
---
Our Vision?
To flatten the playing field.
To transform DeFi × AI from privilege to public good.
To onboard 10,000× more users and unlock 10,000× more activity — starting with a single mint.
"DeFAI Mint" is where philosophy meets finance.
Where belief becomes strategy.
Where conviction becomes capital.
Mint once. Let it invest. Live your life.
Deepfake Phishing: A New Frontier in Cyber ThreatsRaviKumar256934
n today’s hyper-connected digital world, cybercriminals continue to develop increasingly sophisticated methods of deception. Among these, deepfake phishing represents a chilling evolution—a combination of artificial intelligence and social engineering used to exploit trust and compromise security.
Deepfake technology, once a novelty used in entertainment, has quickly found its way into the toolkit of cybercriminals. It allows for the creation of hyper-realistic synthetic media, including images, audio, and videos. When paired with phishing strategies, deepfakes can become powerful weapons of fraud, impersonation, and manipulation.
This document explores the phenomenon of deepfake phishing, detailing how it works, why it’s dangerous, and how individuals and organizations can defend themselves against this emerging threat.
The TRB AJE35 RIIM Coordination and Collaboration Subcommittee has organized a series of webinars focused on building coordination, collaboration, and cooperation across multiple groups. All webinars have been recorded and copies of the recording, transcripts, and slides are below. These resources are open-access following creative commons licensing agreements. The files may be found, organized by webinar date, below. The committee co-chairs would welcome any suggestions for future webinars. The support of the AASHTO RAC Coordination and Collaboration Task Force, the Council of University Transportation Centers, and AUTRI’s Alabama Transportation Assistance Program is gratefully acknowledged.
This webinar overviews proven methods for collaborating with USDOT University Transportation Centers (UTCs), emphasizing state departments of transportation and other stakeholders. It will cover partnerships at all UTC stages, from the Notice of Funding Opportunity (NOFO) release through proposal development, research and implementation. Successful USDOT UTC research, education, workforce development, and technology transfer best practices will be highlighted. Dr. Larry Rilett, Director of the Auburn University Transportation Research Institute will moderate.
For more information, visit: https://aub.ie/trbwebinars
2. Algorithms
What is an algorithm?
An algorithm is a finite set of precise instructions for performing a
computation or for solving a problem.
3. Algorithms
Properties of algorithms:
• Input from a specified set,
• Output from a specified set (solution),
• Definiteness of every step in the computation,
• Correctness of output for every possible input,
• Finiteness of the number of calculation steps,
• Effectiveness of each calculation step and
• Generality for a class of problems.
4. Pseudocode
An algorithm can also be described using a computer language.
Understanding an algorithm is complicated and difficult.
Algorithm can be translated into any programming language. As
many programming languages are in common use, so instead of
using a particular computer language to specify algorithms, a form
of Pseudocode, will be used in this book.
Pseudocode provides an intermediate step between an English
language description of an algorithm and an implementation of this
algorithm in a programming language.
5. Pseudocode
Algorithm 1: Finding the Maximum Element in a Finite Sequence.
procedure max(a1, a2, . . . , an: integers)
max := a1
for i := 2 to n
if max < ai then max := ai
return max{max is the largest element}
7. Linear Search:
It is also called sequential search. It searches an element
sequentially by comparing the searching element with each
element in the list one by one.
procedure linear search(x: integer, a1, a2, . . . , an: distinct integers)
i := 1
while (i ≤ n and x ≠ ai )
i := i + 1
if i ≤ n then location := i
else location := 0
return location {location is the subscript of the term that equals x,
or is 0 if x is not found}
11. Binary Search:
Binary Search algorithm is used to search an element from a list of
elements.
This algorithm can be used when the list has terms occurring in
increasing order.
It proceeds by comparing the element to be located to the middle
term of the list.
The list is then split into two smaller sub lists of same size, or one of
these smaller lists has one fewer term than other.
The search continues by restricting the search to the appropriate
sub lists based on the comparison of the element to be located and
the middle term.
12. Binary Search:
procedure binary search (x: integer, a1, a2, . . . , an: increasing integers)
i := 1{i is left endpoint of search interval}
j := n {j is right endpoint of search interval}
while i < j
m :=
if x > am then i := m + 1
else j := m
if x = ai then location := i
else location := 0
return location{location is the subscript i of the term ai equal to x, or 0 if x is not
found}
13. Binary Search:
Search 18 from sequence 2 , 3 , 5 , 8 , 10 , 15 , 18 , 30
i m j
2 3 5 8 10 15 18 30
As 18>8 and i<j so
i m j
2 3 5 8 10 15 18 30
As 18>15 and i<j so
i m j
2 3 5 8 10 15 18 30
Element found at location 7
14. Binary Search:
Search 3 from sequence 2 , 3 , 5 , 8 , 10 , 15 , 18 , 30
i m j
2 3 5 8 10 15 18 30
As 3<8 and i<j so
i m j
2 3 5 8 10 15 18 30
Element found at location 2
15. Binary Search:
Search 6 from sequence 2 , 3 , 5 , 8 , 10 , 15 , 18 , 30
i m j
2 3 5 8 10 15 18 30
As 6<8 and i<j so
i m j
2 3 5 8 10 15 18 30
6>3 and i<j so
i m j
2 3 5 8 10 15 18 30
6>5 and i<j
i m j
2 3 5 8 10 15 18 30
As i=j so element not found in the list
16. Sorting
Sorting is putting the elements into a list in which the elements are in
increasing order.
17. Bubble Sort:
The bubble sort is one of the simplest sorting algorithms, but not one
of the most efficient.
It puts a list into increasing order by successively comparing
adjacent elements, interchanging them if they are in the wrong
order.
To carry out the bubble sort, we perform the basic operation that is,
interchanging a larger element with a smaller one following it,
starting at the beginning of the list, for full pass.
We iterate this procedure until the sort is complete.
18. Bubble Sort:
procedure bubblesort(a1, . . . , an : real numbers with n ≥ 2)
for i := 1 to n − 1
for j := 1 to n − i
if aj > aj+1 then interchange aj and aj+1
{a1, . . . , an is in increasing order}
22. Insertion Sort:
Insertion Sort is a simple sorting algorithm, but it is usually not the most
efficient.
To sort a list with n elements, the insertion sort begins with the second
element.
This second element is compared with the first element and inserted
before the first element if it is smaller than the first element and after
the first element if it is greater than the first element.
At this point, the first two elements are in correct order.
The third element is then compared with the first element, and if it is
larger than the first element, it is compared with the second element,
it is inserted into the correct position among the first three elements
and so on.
23. Insertion Sort:
procedure insertion sort(a1, a2, . . . , an: real numbers with n ≥ 2)
for j := 2 to n
i := 1
while aj > ai
i := i + 1
m := aj
for k := 0 to j − i − 1
aj−k := aj−k−1
ai := m
{a1, . . . , an is in increasing order}