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Dynamic programming is a recursive optimization technique used to solve problems with interrelated decisions. It breaks the problem down into sequential steps, where each step builds on the solutions to previous steps. The optimal solution is determined by working through each step in order. Dynamic programming has advantages like computational savings over complete enumeration and providing insight into problem nature. However, it also has disadvantages like requiring more expertise, lacking general algorithms, and facing dimensionality problems for applications with multiple states.

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Problems, Problem spaces and Search

The document discusses state space search problems and techniques for solving them. It defines state space search as a process of considering successive configurations or states of a problem instance to find a goal state. Various search techniques like breadth-first search, depth-first search, and heuristic search are described. It also discusses problem characteristics that help determine the most appropriate search method, such as whether a problem can be decomposed or solution steps ignored/undone. Examples of search problems like the 8-puzzle, chess, and water jug problems are provided to illustrate state space formulation and solutions.

Flowshop scheduling

This document discusses flow shop scheduling, which involves sequencing jobs through a set of machines or processes where each job must visit the machines in the same order. It defines flow shop scheduling and provides examples of its applications in industries that require a strict production order. The document then describes two common methods for flow shop scheduling - preemptive and non-preemptive - and provides an example to illustrate the difference. Finally, it discusses algorithms for solving flow shop scheduling problems, focusing on Johnson's algorithm, which provides an optimal solution for problems with two machines.

Dynamic Programming

Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.

Improving of software processes

This document discusses software project management. It outlines software processes, common problems, and methods for improving processes. Software processes involve many elements and sub-processes. Common problems include cost overruns, schedule delays, low productivity, and poor quality. There are three methods for improving processes: meta processes focus on organizational strategies and profitability, macro processes produce software within constraints for a project, and micro processes focus on iterations and risk resolution for a project team. The objective of process improvement is to maximize resources for productive activities and minimize overhead impacts on resources like personnel and schedule to ultimately enhance product quality.

Dynamic programming

Dynamic Programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions using a memory-based data structure (array, map,etc).

Local search algorithm

Local search algorithms operate by examining the current node and its neighbors. They are suitable for problems where the solution is the goal state itself rather than the path to get there. Hill-climbing and simulated annealing are examples of local search algorithms. Hill-climbing continuously moves to higher value neighbors until a local peak is reached. Simulated annealing also examines random moves and can accept moves to worse states based on probability. Both aim to find an optimal or near-optimal solution but can get stuck in local optima.

Dynamic programming

A. Plannig
The document mentions that in dynamic programming, "Programming" refers to "planning", not computer programming.

Recognition-of-tokens

Relational operator transition diagram, transition diagram of identifiers or digits, Token Recognition, rules to specify and recognize token

Problems, Problem spaces and Search

The document discusses state space search problems and techniques for solving them. It defines state space search as a process of considering successive configurations or states of a problem instance to find a goal state. Various search techniques like breadth-first search, depth-first search, and heuristic search are described. It also discusses problem characteristics that help determine the most appropriate search method, such as whether a problem can be decomposed or solution steps ignored/undone. Examples of search problems like the 8-puzzle, chess, and water jug problems are provided to illustrate state space formulation and solutions.

Flowshop scheduling

This document discusses flow shop scheduling, which involves sequencing jobs through a set of machines or processes where each job must visit the machines in the same order. It defines flow shop scheduling and provides examples of its applications in industries that require a strict production order. The document then describes two common methods for flow shop scheduling - preemptive and non-preemptive - and provides an example to illustrate the difference. Finally, it discusses algorithms for solving flow shop scheduling problems, focusing on Johnson's algorithm, which provides an optimal solution for problems with two machines.

Dynamic Programming

Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.

Improving of software processes

This document discusses software project management. It outlines software processes, common problems, and methods for improving processes. Software processes involve many elements and sub-processes. Common problems include cost overruns, schedule delays, low productivity, and poor quality. There are three methods for improving processes: meta processes focus on organizational strategies and profitability, macro processes produce software within constraints for a project, and micro processes focus on iterations and risk resolution for a project team. The objective of process improvement is to maximize resources for productive activities and minimize overhead impacts on resources like personnel and schedule to ultimately enhance product quality.

Dynamic programming

Dynamic Programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions using a memory-based data structure (array, map,etc).

Local search algorithm

Local search algorithms operate by examining the current node and its neighbors. They are suitable for problems where the solution is the goal state itself rather than the path to get there. Hill-climbing and simulated annealing are examples of local search algorithms. Hill-climbing continuously moves to higher value neighbors until a local peak is reached. Simulated annealing also examines random moves and can accept moves to worse states based on probability. Both aim to find an optimal or near-optimal solution but can get stuck in local optima.

Dynamic programming

A. Plannig
The document mentions that in dynamic programming, "Programming" refers to "planning", not computer programming.

Recognition-of-tokens

Relational operator transition diagram, transition diagram of identifiers or digits, Token Recognition, rules to specify and recognize token

Issues in knowledge representation

Knowledge representation techniques face several issues including representing important attributes of objects, relationships between attributes, choosing the level of detail in representations, depicting sets of multiple objects, and determining appropriate structures as needed.

I. Hill climbing algorithm II. Steepest hill climbing algorithm

Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists

I. AO* SEARCH ALGORITHM

Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists

Processor allocation in Distributed Systems

Allocation of processors to processes in Distributed Systems. Strategies or algorithms for processor allocation. Design and Implementation Issues of Strategies.

Traveling salesman problem

The document describes the traveling salesman problem (TSP) and how to solve it using a branch and bound approach. The TSP aims to find the shortest route for a salesman to visit each city once and return to the starting city. It can be represented as a weighted graph. The branch and bound method involves reducing the cost matrix by subtracting minimum row/column values, building a state space tree of paths, and choosing the path with the lowest cost at each step. An example demonstrates these steps to find the optimal solution of 24 for a 5 city TSP problem.

Planning in AI(Partial order planning)

this presentation is about planning process in AI. The presentation specifically explained POP(Partial order Planning). There are also another planning. In this presentation with help of an example the presentation is briefly explained the planning is done in AI

PAC Learning

This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.

Heap Management

The document discusses heap memory management. It describes the heap as the portion of memory used for indefinitely stored data. A memory manager subsystem allocates and deallocates space in the heap. It keeps track of free space and serves as the interface between programs and the operating system. When allocating memory, the manager either uses available contiguous space or increases heap size from the OS. Deallocated space is returned to the free pool but memory is not returned to the OS when heap usage drops.

Elements of dynamic programming

An introductory project showing how to identify if a DP solution to a problem exists. It also discusses the essential parts of DP solutions briefly.

Timestamp protocols

This document provides an overview of timestamp protocols in database management systems. It discusses how timestamps are generated and used to order transactions. The basic timestamp ordering protocol checks timestamps on read and write operations to ensure serializability. Strict timestamp ordering delays some transactions to ensure schedules are both serializable and strict. Multiversion timestamp ordering uses multiple versions of data items to allow reads to always succeed while maintaining serializability.

Dynamic programming class 16

Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller overlapping subproblems and storing the results of already solved subproblems, rather than recomputing them. It is applicable to problems exhibiting optimal substructure and overlapping subproblems. The key steps are to define the optimal substructure, recursively define the optimal solution value, compute values bottom-up, and optionally reconstruct the optimal solution. Common examples that can be solved with dynamic programming include knapsack, shortest paths, matrix chain multiplication, and longest common subsequence.

COMPILER DESIGN Run-Time Environments

Run-Time Environments: Storage organization, Stack Allocation of Space, Access to Nonlocal Data on the Stack, Heap Management, Introduction to Garbage Collection, Introduction to Trace-Based Collection. Code Generation: Issues in the Design of a Code Generator, The Target Language, Addresses in the Target Code, Basic Blocks and Flow Graphs, Optimization of Basic Blocks, A Simple Code Generator, Peephole Optimization, Register Allocation and Assignment, Dynamic Programming Code-Generation

Minmax Algorithm In Artificial Intelligence slides

Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.

Loop optimization

Loop optimization is a technique to improve the performance of programs by optimizing the inner loops which take a large amount of time. Some common loop optimization methods include code motion, induction variable and strength reduction, loop invariant code motion, loop unrolling, and loop fusion. Code motion moves loop-invariant code outside the loop to avoid unnecessary computations. Induction variable and strength reduction techniques optimize computations involving induction variables. Loop invariant code motion avoids repeating computations inside loops. Loop unrolling replicates loop bodies to reduce loop control overhead. Loop fusion combines multiple nested loops to reduce the total number of iterations.

program partitioning and scheduling IN Advanced Computer Architecture

Advanced Computer Architecture,Program Partitioning and Scheduling,Program Partitioning & Scheduling,Latency,Levels of Parallelism,Loop-level Parallelism,Subprogram-level Parallelism,Job or Program-Level Parallelism,Communication Latency,Grain Packing and Scheduling,Program Graphs and Packing

Peephole Optimization

This presentation discusses peephole optimization. Peephole optimization is performed on small segments of generated code to replace sets of instructions with shorter or faster equivalents. It aims to improve performance, reduce code size, and reduce memory footprint. The working flow of peephole optimization involves scanning code for patterns that match predefined replacement rules. These rules include constant folding, strength reduction, removing null sequences, and combining operations. Peephole optimization functions by replacing slow instructions with faster ones, removing redundant code and stack instructions, and optimizing jumps.

COCOMO Model in software project management

what is COCOMO Model in software project management
COCOMO Model in software project management defined
COCOMO Model in software project management
what is cocomo model
cocomo model and its application

Error Detection & Recovery

This ppt is about error handling in the compiler design. It also states the error recovering strategies.

search strategies in artificial intelligence

The document discusses search strategies in artificial intelligence. It defines key terms like search space, start state, goal test, and search tree. It describes properties of search algorithms like completeness, optimality, time complexity, and space complexity. It differentiates between uninformed searches, which do not use domain knowledge, like breadth-first search and depth-first search, and informed searches, which use heuristics to guide the search more efficiently, like greedy search and A* search. The document outlines the differences between informed and uninformed searches.

Introduction to dynamic programming

Dynamic programming is an algorithm design technique for optimization problems that reduces time by increasing space usage. It works by breaking problems down into overlapping subproblems and storing the solutions to subproblems, rather than recomputing them, to build up the optimal solution. The key aspects are identifying the optimal substructure of problems and handling overlapping subproblems in a bottom-up manner using tables. Examples that can be solved with dynamic programming include the knapsack problem, shortest paths, and matrix chain multiplication.

Building blocks of Algblocks of Alg.pptx

This document discusses the key building blocks of algorithms, including problem solving techniques, the problem solving process, and common algorithm structures. It describes algorithms as step-by-step procedures to solve problems and introduces common algorithm structures like sequences, selections, and iterations. It also discusses the basic components of algorithms, such as statements, state, control flow, and functions. Functions are described as reusable blocks of code that perform specific tasks to simplify complex problems.

PROBLEM SOLVING TECHNIQUES

The document provides an overview of problem solving techniques in computer science. It discusses:
- Algorithms and programs as solutions to problems expressed in programming languages.
- Key aspects of problem solving including problem definition, getting started, working backwards, and utilizing past experiences.
- General problem solving strategies such as "divide and conquer" and dynamic programming.
- Algorithm design techniques like top-down design and choosing appropriate data structures.
- Implementing algorithms through modularization, variable naming, documentation, debugging, and testing.
- Verifying algorithms through input/output assertions, symbolic execution, and proving program segments.
- Analyzing algorithm efficiency by reducing redundant computations, early termination detection, and trading storage for

Issues in knowledge representation

Knowledge representation techniques face several issues including representing important attributes of objects, relationships between attributes, choosing the level of detail in representations, depicting sets of multiple objects, and determining appropriate structures as needed.

I. Hill climbing algorithm II. Steepest hill climbing algorithm

Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists

I. AO* SEARCH ALGORITHMArtificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists

Processor allocation in Distributed Systems

Allocation of processors to processes in Distributed Systems. Strategies or algorithms for processor allocation. Design and Implementation Issues of Strategies.

Traveling salesman problem

The document describes the traveling salesman problem (TSP) and how to solve it using a branch and bound approach. The TSP aims to find the shortest route for a salesman to visit each city once and return to the starting city. It can be represented as a weighted graph. The branch and bound method involves reducing the cost matrix by subtracting minimum row/column values, building a state space tree of paths, and choosing the path with the lowest cost at each step. An example demonstrates these steps to find the optimal solution of 24 for a 5 city TSP problem.

Planning in AI(Partial order planning)

this presentation is about planning process in AI. The presentation specifically explained POP(Partial order Planning). There are also another planning. In this presentation with help of an example the presentation is briefly explained the planning is done in AI

PAC Learning

This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.

Heap Management

The document discusses heap memory management. It describes the heap as the portion of memory used for indefinitely stored data. A memory manager subsystem allocates and deallocates space in the heap. It keeps track of free space and serves as the interface between programs and the operating system. When allocating memory, the manager either uses available contiguous space or increases heap size from the OS. Deallocated space is returned to the free pool but memory is not returned to the OS when heap usage drops.

Elements of dynamic programming

An introductory project showing how to identify if a DP solution to a problem exists. It also discusses the essential parts of DP solutions briefly.

Timestamp protocols

This document provides an overview of timestamp protocols in database management systems. It discusses how timestamps are generated and used to order transactions. The basic timestamp ordering protocol checks timestamps on read and write operations to ensure serializability. Strict timestamp ordering delays some transactions to ensure schedules are both serializable and strict. Multiversion timestamp ordering uses multiple versions of data items to allow reads to always succeed while maintaining serializability.

Dynamic programming class 16

Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller overlapping subproblems and storing the results of already solved subproblems, rather than recomputing them. It is applicable to problems exhibiting optimal substructure and overlapping subproblems. The key steps are to define the optimal substructure, recursively define the optimal solution value, compute values bottom-up, and optionally reconstruct the optimal solution. Common examples that can be solved with dynamic programming include knapsack, shortest paths, matrix chain multiplication, and longest common subsequence.

COMPILER DESIGN Run-Time Environments

Run-Time Environments: Storage organization, Stack Allocation of Space, Access to Nonlocal Data on the Stack, Heap Management, Introduction to Garbage Collection, Introduction to Trace-Based Collection. Code Generation: Issues in the Design of a Code Generator, The Target Language, Addresses in the Target Code, Basic Blocks and Flow Graphs, Optimization of Basic Blocks, A Simple Code Generator, Peephole Optimization, Register Allocation and Assignment, Dynamic Programming Code-Generation

Minmax Algorithm In Artificial Intelligence slides

Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.

Loop optimization

Loop optimization is a technique to improve the performance of programs by optimizing the inner loops which take a large amount of time. Some common loop optimization methods include code motion, induction variable and strength reduction, loop invariant code motion, loop unrolling, and loop fusion. Code motion moves loop-invariant code outside the loop to avoid unnecessary computations. Induction variable and strength reduction techniques optimize computations involving induction variables. Loop invariant code motion avoids repeating computations inside loops. Loop unrolling replicates loop bodies to reduce loop control overhead. Loop fusion combines multiple nested loops to reduce the total number of iterations.

program partitioning and scheduling IN Advanced Computer Architecture

Advanced Computer Architecture,Program Partitioning and Scheduling,Program Partitioning & Scheduling,Latency,Levels of Parallelism,Loop-level Parallelism,Subprogram-level Parallelism,Job or Program-Level Parallelism,Communication Latency,Grain Packing and Scheduling,Program Graphs and Packing

Peephole Optimization

This presentation discusses peephole optimization. Peephole optimization is performed on small segments of generated code to replace sets of instructions with shorter or faster equivalents. It aims to improve performance, reduce code size, and reduce memory footprint. The working flow of peephole optimization involves scanning code for patterns that match predefined replacement rules. These rules include constant folding, strength reduction, removing null sequences, and combining operations. Peephole optimization functions by replacing slow instructions with faster ones, removing redundant code and stack instructions, and optimizing jumps.

COCOMO Model in software project management

what is COCOMO Model in software project management
COCOMO Model in software project management defined
COCOMO Model in software project management
what is cocomo model
cocomo model and its application

Error Detection & Recovery

This ppt is about error handling in the compiler design. It also states the error recovering strategies.

search strategies in artificial intelligence

The document discusses search strategies in artificial intelligence. It defines key terms like search space, start state, goal test, and search tree. It describes properties of search algorithms like completeness, optimality, time complexity, and space complexity. It differentiates between uninformed searches, which do not use domain knowledge, like breadth-first search and depth-first search, and informed searches, which use heuristics to guide the search more efficiently, like greedy search and A* search. The document outlines the differences between informed and uninformed searches.

Introduction to dynamic programming

Dynamic programming is an algorithm design technique for optimization problems that reduces time by increasing space usage. It works by breaking problems down into overlapping subproblems and storing the solutions to subproblems, rather than recomputing them, to build up the optimal solution. The key aspects are identifying the optimal substructure of problems and handling overlapping subproblems in a bottom-up manner using tables. Examples that can be solved with dynamic programming include the knapsack problem, shortest paths, and matrix chain multiplication.

Issues in knowledge representation

Issues in knowledge representation

I. Hill climbing algorithm II. Steepest hill climbing algorithm

I. Hill climbing algorithm II. Steepest hill climbing algorithm

I. AO* SEARCH ALGORITHM

I. AO* SEARCH ALGORITHM

Processor allocation in Distributed Systems

Processor allocation in Distributed Systems

Traveling salesman problem

Traveling salesman problem

Planning in AI(Partial order planning)

Planning in AI(Partial order planning)

PAC Learning

PAC Learning

Heap Management

Heap Management

Elements of dynamic programming

Elements of dynamic programming

Timestamp protocols

Timestamp protocols

Dynamic programming class 16

Dynamic programming class 16

COMPILER DESIGN Run-Time Environments

COMPILER DESIGN Run-Time Environments

Minmax Algorithm In Artificial Intelligence slides

Minmax Algorithm In Artificial Intelligence slides

Loop optimization

Loop optimization

program partitioning and scheduling IN Advanced Computer Architecture

program partitioning and scheduling IN Advanced Computer Architecture

Peephole Optimization

Peephole Optimization

COCOMO Model in software project management

COCOMO Model in software project management

Error Detection & Recovery

Error Detection & Recovery

search strategies in artificial intelligence

search strategies in artificial intelligence

Introduction to dynamic programming

Introduction to dynamic programming

Building blocks of Algblocks of Alg.pptx

This document discusses the key building blocks of algorithms, including problem solving techniques, the problem solving process, and common algorithm structures. It describes algorithms as step-by-step procedures to solve problems and introduces common algorithm structures like sequences, selections, and iterations. It also discusses the basic components of algorithms, such as statements, state, control flow, and functions. Functions are described as reusable blocks of code that perform specific tasks to simplify complex problems.

PROBLEM SOLVING TECHNIQUES

The document provides an overview of problem solving techniques in computer science. It discusses:
- Algorithms and programs as solutions to problems expressed in programming languages.
- Key aspects of problem solving including problem definition, getting started, working backwards, and utilizing past experiences.
- General problem solving strategies such as "divide and conquer" and dynamic programming.
- Algorithm design techniques like top-down design and choosing appropriate data structures.
- Implementing algorithms through modularization, variable naming, documentation, debugging, and testing.
- Verifying algorithms through input/output assertions, symbolic execution, and proving program segments.
- Analyzing algorithm efficiency by reducing redundant computations, early termination detection, and trading storage for

Csc 102 lecture note(introduction to problem solving)

1. The document provides an introduction to problem solving concepts and algorithms. It discusses general problem solving strategies like understanding the problem, devising a plan, carrying out the plan, and looking back.
2. It defines an algorithm as a set of steps to solve a problem and discusses the major components of algorithms - input, processing, and output. Properties of algorithms like finiteness, definiteness, effectiveness, and generality are also outlined.
3. The role of algorithms in problem solving is described as helping to evaluate if a problem can be solved using a computer and identifying the steps, decisions, and variables needed for the problem.

Unit 1 python (2021 r)

This document provides an overview of problem solving and Python programming. It discusses computational thinking and problem solving, including identifying computational problems, algorithms, building blocks of algorithms, and illustrative problems. It also discusses algorithmic problem solving techniques like iteration and recursion. Finally, it briefly introduces the course titled "GE8151-PROBLEM SOLVING AND PYTHON PROGRAMMING".

Dynamic programming prasintation eaisy

Dynamic programming is a technique for solving complex problems by breaking them down into simpler sub-problems. It involves storing solutions to sub-problems for later use, avoiding recomputing them. Examples where it can be applied include matrix chain multiplication and calculating Fibonacci numbers. For matrix chains, dynamic programming finds the optimal order for multiplying matrices with minimum computations. For Fibonacci numbers, it calculates values in linear time by storing previous solutions rather than exponentially recomputing them through recursion.

A brief study on linear programming solving methods

This document summarizes linear programming and two methods for solving linear programming problems: the graphical method and the simplex method. It outlines the key components of linear programming problems including decision variables, objective functions, and constraints. It then describes the steps of the graphical method and simplex method in solving linear programming problems. The graphical method involves plotting the feasible region and objective function on a graph to find the optimal point. The simplex method uses an algebraic table approach to iteratively find the optimal solution.

2-Algorithms and Complexit data structurey.pdf

The document discusses algorithms design and complexity analysis. It defines an algorithm as a well-defined sequence of steps to solve a problem and notes that algorithms always take inputs and produce outputs. It discusses different approaches to designing algorithms like greedy, divide and conquer, and dynamic programming. It also covers analyzing algorithm complexity using asymptotic analysis by counting the number of basic operations and deriving the time complexity function in terms of input size.

Dynamic programming 2

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is applicable when problems exhibit overlapping subproblems that are only slightly smaller. The method involves 4 steps: 1) developing a mathematical notation to express solutions, 2) proving the principle of optimality holds, 3) deriving a recurrence relation relating solutions to subsolutions, and 4) writing an algorithm to compute the recurrence relation. Dynamic programming yields optimal solutions when the principle of optimality holds, without needing to prove optimality. It is used to solve production, scheduling, resource allocation, and inventory problems.

Operation Research Techniques

This document presents 15 quantitative techniques and tools: Linear Programming, Queuing Theory, Inventory Control Method, Net Work Analysis, Replacement Problems, Sequencing, Integer Programming, Assignment Problems, Transportation Problems, Decision Theory and Game Theory, Markov Analysis, Simulation, Dynamic Programming, Goal Programming, and Symbolic Logic. It provides a brief overview of each technique, describing its purpose and typical applications.

C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES

The document discusses various topics related to unit 1 of a programming course, including flowcharts, algorithm design using top-down and bottom-up approaches, and pseudocode. It covers elements of flowcharts like input/output, branching, and iteration. It describes problem solving in two phases - problem solving and implementation. It also explains algorithm development, properties of algorithms, and constructs like sequence, decision, and iteration. Pseudocode representation and writing pseudocode from algorithms and flowcharts is also mentioned.

CH-1.1 Introduction (1).pptx

This document provides an introduction to the analysis of algorithms. It discusses algorithm specification, performance analysis frameworks, and asymptotic notations used to analyze algorithms. Key aspects covered include time complexity, space complexity, worst-case analysis, and average-case analysis. Common algorithms like sorting and searching are also mentioned. The document outlines algorithm design techniques such as greedy methods, divide and conquer, and dynamic programming. It distinguishes between recursive and non-recursive algorithms and provides examples of complexity analysis for non-recursive algorithms.

Unit.2. linear programming

This document discusses linear programming and its concepts, formulation, and methods of solving linear programming problems. It provides the following key points:
1) Linear programming involves optimizing a linear objective function subject to linear constraints. It aims to find the best allocation of limited resources to achieve objectives.
2) Formulating a linear programming problem involves identifying decision variables, the objective function, and constraints. Problems can be solved graphically or algebraically using the simplex method.
3) The graphic method can be used for problems with two variables, involving plotting the constraints on a graph to find the optimal solution at a corner point of the feasible region.

Module 2ppt.pptx divid and conquer method

This document discusses dynamic programming and provides examples of problems that can be solved using dynamic programming. It covers the following key points:
- Dynamic programming can be used to solve problems that exhibit optimal substructure and overlapping subproblems. It works by breaking problems down into subproblems and storing the results of subproblems to avoid recomputing them.
- Examples of problems discussed include matrix chain multiplication, all pairs shortest path, optimal binary search trees, 0/1 knapsack problem, traveling salesperson problem, and flow shop scheduling.
- The document provides pseudocode for algorithms to solve matrix chain multiplication and optimal binary search trees using dynamic programming. It also explains the basic steps and principles of dynamic programming algorithm design

Paper review: Learned Optimizers that Scale and Generalize.

The paper proposes a novel hierarchical RNN architecture for a learned optimizer that aims to address scalability and generalization issues. The architecture uses a hierarchical structure of parameter, tensor, and global RNNs to enable coordination of updates across parameters with low memory and computation costs. It also incorporates features inspired by hand-designed optimizers like computing gradients at attended locations and dynamic input scaling to provide the learned optimizer with useful information. The optimizer is meta-trained on diverse small problems and can generalize to optimizing new problem types, though it struggles on very large models. Ablation studies show the importance of the paper's design choices for the learned optimizer's performance.

linear programming

The document discusses linear programming, including:
1. It describes the basic concepts of linear programming, such as decision variables, constraints, and the objective function needing to be linear.
2. It explains the steps to formulate a linear programming problem, such as identifying decision variables and constraints, and writing the objective function and constraints as linear combinations of the variables.
3. It provides examples of how to write linear programming problems in standard form to maximize or minimize objectives subject to constraints.

Glenn Vanderburg — Real software engineering

The document discusses software engineering and argues that an implementation plan focused only on analysis and coding steps is "doomed to failure" for developing large software systems. It presents a model with additional steps like requirements analysis, program design, testing, and operations. However, it notes that separating these steps risks major redesigns if issues are found between steps.
The document proposes an iterative model where each step involves feedback and iteration with preceding and succeeding steps. This allows issues to be addressed within manageable scope rather than requiring large redesigns. It argues five additional features must be added to fully eliminate development risks, and notes the importance of establishing requirements and having a "firm and close-coupled moving baseline" to fall back

Real software engineering

The document discusses the differences between traditional notions of software engineering and a more empirical "real software engineering" approach. It argues that software is different from other engineering domains due to its complexity, unpredictability, and ability to change rapidly through testing and experimentation. A defined, documentation-heavy process is not suitable for software. Instead, an empirical approach using techniques like pair programming, short iterations, and frequent testing allows software to be engineered effectively through a process of continuous adaptation.

Operating system 23 process synchronization

Processes can execute concurrently
May be interrupted at any time, partially completing execution
Concurrent access to shared data may result in data inconsistency
Maintaining data consistency requires mechanisms to ensure the orderly execution of cooperating processes
Illustration of the problem:Suppose that we wanted to provide a solution to the consumer-producer problem that fills all the buffers. We can do so by having an integer counter that keeps track of the number of full buffers. Initially, counter is set to 0. It is incremented by the producer after it produces a new buffer and is decremented by the consumer after it consumes a buffer

Design & Analysis of Algorithm course .pptx

This document discusses algorithms and their analysis. It defines an algorithm as a well-defined computational procedure that takes inputs and produces outputs. Key characteristics of algorithms include definiteness, finiteness, effectiveness, correctness, simplicity, and unambiguousness. The document discusses two common algorithm design techniques: divide and conquer, which divides a problem into subproblems, and greedy techniques, which make locally optimal choices to find a near-optimal solution. It also covers analyzing algorithms, including asymptotic time and space complexity analysis to determine how resource usage grows with problem size.

An efficient use of temporal difference technique in Computer Game Learning

This document summarizes an efficient use of temporal difference techniques in computer game learning. It discusses reinforcement learning and some key concepts including the agent-environment interface, types of reinforcement learning tasks, elements of reinforcement learning like policy, reward functions, and value functions. It also describes algorithms like dynamic programming, policy iteration, value iteration, and temporal difference learning. Finally, it mentions some applications of reinforcement learning in benchmark problems, games, and real-world domains like robotics and control.

Building blocks of Algblocks of Alg.pptx

Building blocks of Algblocks of Alg.pptx

PROBLEM SOLVING TECHNIQUES

PROBLEM SOLVING TECHNIQUES

Csc 102 lecture note(introduction to problem solving)

Csc 102 lecture note(introduction to problem solving)

Unit 1 python (2021 r)

Unit 1 python (2021 r)

Dynamic programming prasintation eaisy

Dynamic programming prasintation eaisy

A brief study on linear programming solving methods

A brief study on linear programming solving methods

2-Algorithms and Complexit data structurey.pdf

2-Algorithms and Complexit data structurey.pdf

Dynamic programming 2

Dynamic programming 2

Operation Research Techniques

Operation Research Techniques

C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES

C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES

CH-1.1 Introduction (1).pptx

CH-1.1 Introduction (1).pptx

Unit.2. linear programming

Unit.2. linear programming

Module 2ppt.pptx divid and conquer method

Module 2ppt.pptx divid and conquer method

Paper review: Learned Optimizers that Scale and Generalize.

Paper review: Learned Optimizers that Scale and Generalize.

linear programming

linear programming

Glenn Vanderburg — Real software engineering

Glenn Vanderburg — Real software engineering

Real software engineering

Real software engineering

Operating system 23 process synchronization

Operating system 23 process synchronization

Design & Analysis of Algorithm course .pptx

Design & Analysis of Algorithm course .pptx

An efficient use of temporal difference technique in Computer Game Learning

An efficient use of temporal difference technique in Computer Game Learning

Competition Act, 2002

The document discusses competition law and policy in India. It provides definitions of key concepts like competition and dominant position. It describes the objectives of competition law as promoting economic efficiency and consumer welfare. The Competition Act 2002 aims to prevent anti-competitive practices by firms like cartels, abuse of dominance, and regulate mergers and acquisitions. It established the Competition Commission of India as a regulatory body with quasi-judicial powers.

Statistics All

The document describes 14 cases involving statistical analyses. Case I involves calculating the probability of at least 1300 or between 1250-1300 students passing a difficult test, given the individual student probability of passing. Case II involves probabilities related to the length of life of dry battery cells based on mean and standard deviation. Case III involves calculating the probability that a sample mean of accounts receivables will be less than a given value.

software engineering

The document discusses software engineering and provides definitions and classifications of software. It defines software as a set of programs and documentation that activate hardware to perform tasks. Software is classified as generic or customized and described in categories such as system software, business software, design software, embedded software, and artificial intelligence. The roles and skills of a system analyst/software engineer are also outlined, including technical skills like analysis, design, and project management as well as interpersonal skills. Finally, the document discusses the system development life cycle (SDLC) process.

Iti Lprocessmgmt

The document provides an overview of ITIL (Information Technology Infrastructure Library) service management processes. ITIL processes are divided into two main components: Service Support which focuses on day-to-day operations, and Service Delivery which focuses on long-term planning and improvement. Key processes include Change Management, Release Management, Incident Management, Problem Management, and Configuration Management. The goals, definitions, and activities of each process are described at a high level.

It Service Management

IT service management focuses on managing information technology systems from the customer's perspective rather than a technology-centered approach. It has two main components: service support, which includes five processes for day-to-day operations, and service delivery, which includes five processes for long-term planning and improvement of IT services. The overall goals of IT service management are to ensure IT services meet customer needs, improve service availability and stability, enhance communication, and increase efficiency of internal processes.

Security And Ethical Challenges Of Infornation Technology

This document discusses several security and ethical challenges of information technology. It identifies issues around employment, privacy, health, and more. It also describes different types of computer crimes like hacking, cyber theft, and software piracy. Additionally, it outlines security measures companies use like encryption, firewalls, email monitoring, and biometric controls to help manage security and privacy risks.

Dm Ps Analysis

The document discusses managerial decision making and problem solving. It covers several topics:
1) The types of decisions are programmed and non-programmed decisions. Programmed decisions follow rules while non-programmed decisions are unique situations.
2) There are six steps to the decision making process: identifying problems, gathering information, developing alternatives, evaluating alternatives, selecting an alternative, and implementing and monitoring the decision.
3) Decision support systems are tools to aid decision making, especially for non-programmed decisions. Different models in decision support systems are discussed.

Decision Support System

Decision support systems (DSS) are a class of computerized systems that help organizational decision-making. A DSS compiles useful information from data, documents, and business models to help decision-makers identify and solve problems. It has three key functions: capturing past information, data processing, and data retrieval. A DSS has three core components - a database management system, model-based management system, and dialog generation/management system. There are different types of DSS that aid decision-making through various methods like data, models, knowledge, or documents.

Bis Data Information

The document defines key terms related to data, information, knowledge, and information systems. It states that data are raw facts, information is organized data, and knowledge is information made useful to support tasks or decisions. An information system is described as a set of interrelated components that includes inputs, processing, outputs, and feedback to meet objectives. The value of information is discussed as helping decision makers achieve goals and decide on investments.

Bis Tools Of It

1. The document discusses various concepts related to information systems including information, processes, knowledge, organization systems, and organizational structure.
2. It describes different types of organizational structures like traditional, project-based, team-based, and virtual structures.
3. The document also covers topics like organizational change, re-engineering, technology diffusion, total quality management, and careers in information systems.

Basics Of Networking

This document provides an overview of basic telecommunications and networking concepts. It defines what a computer network is and common types of networks including local area networks (LANs), wide area networks (WANs), metropolitan area networks (MANs), and personal area networks (PANs). It also discusses network topologies, standards, the internet, intranets, extranets, and virtual private networks (VPNs).

Competition Act, 2002

Competition Act, 2002

Environment Act, 1986

Environment Act, 1986

Statistics All

Statistics All

software engineering

software engineering

Iti Lprocessmgmt

Iti Lprocessmgmt

It Service Management

It Service Management

Security And Ethical Challenges Of Infornation Technology

Security And Ethical Challenges Of Infornation Technology

Dm Ps Analysis

Dm Ps Analysis

Decision Support System

Decision Support System

Bis Data Information

Bis Data Information

Bis Tools Of It

Bis Tools Of It

Basics Of Networking

Basics Of Networking

Y-Combinator seed pitch deck template PP

Pitch Deck Template

The Microsoft 365 Migration Tutorial For Beginner.pptx

This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/

Introducing BoxLang : A new JVM language for productivity and modularity!

Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.

Discover the Unseen: Tailored Recommendation of Unwatched Content

The session shares how JioCinema approaches ""watch discounting."" This capability ensures that if a user watched a certain amount of a show/movie, the platform no longer recommends that particular content to the user. Flawless operation of this feature promotes the discover of new content, improving the overall user experience.
JioCinema is an Indian over-the-top media streaming service owned by Viacom18.

GNSS spoofing via SDR (Criptored Talks 2024)

In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.

Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips

ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.

"What does it really mean for your system to be available, or how to define w...

We will talk about system monitoring from a few different angles. We will start by covering the basics, then discuss SLOs, how to define them, and why understanding the business well is crucial for success in this exercise.

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host

Containers & AI - Beauty and the Beast!?!

As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211

Demystifying Knowledge Management through Storytelling

The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer

"Choosing proper type of scaling", Olena Syrota

Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Dmitrii Kamaev, PhD
Senior Product Owner - QIAGEN

Christine's Supplier Sourcing Presentaion.pptx

How I source suppliers

"$10 thousand per minute of downtime: architecture, queues, streaming and fin...

Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.

Session 1 - Intro to Robotic Process Automation.pdf

👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/

JavaLand 2024: Application Development Green Masterplan

My presentation slides I used at JavaLand 2024

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.

Principle of conventional tomography-Bibash Shahi ppt..pptx

before the computed tomography, it had been widely used.

"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.

What is an RPA CoE? Session 2 – CoE Roles

In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems

Y-Combinator seed pitch deck template PP

Y-Combinator seed pitch deck template PP

The Microsoft 365 Migration Tutorial For Beginner.pptx

The Microsoft 365 Migration Tutorial For Beginner.pptx

Introducing BoxLang : A new JVM language for productivity and modularity!

Introducing BoxLang : A new JVM language for productivity and modularity!

Discover the Unseen: Tailored Recommendation of Unwatched Content

Discover the Unseen: Tailored Recommendation of Unwatched Content

GNSS spoofing via SDR (Criptored Talks 2024)

GNSS spoofing via SDR (Criptored Talks 2024)

Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips

Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips

"What does it really mean for your system to be available, or how to define w...

"What does it really mean for your system to be available, or how to define w...

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

Containers & AI - Beauty and the Beast!?!

Containers & AI - Beauty and the Beast!?!

Demystifying Knowledge Management through Storytelling

Demystifying Knowledge Management through Storytelling

"Choosing proper type of scaling", Olena Syrota

"Choosing proper type of scaling", Olena Syrota

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Christine's Supplier Sourcing Presentaion.pptx

Christine's Supplier Sourcing Presentaion.pptx

"$10 thousand per minute of downtime: architecture, queues, streaming and fin...

"$10 thousand per minute of downtime: architecture, queues, streaming and fin...

Session 1 - Intro to Robotic Process Automation.pdf

Session 1 - Intro to Robotic Process Automation.pdf

JavaLand 2024: Application Development Green Masterplan

JavaLand 2024: Application Development Green Masterplan

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

Principle of conventional tomography-Bibash Shahi ppt..pptx

Principle of conventional tomography-Bibash Shahi ppt..pptx

"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

What is an RPA CoE? Session 2 – CoE Roles

What is an RPA CoE? Session 2 – CoE Roles

- 2. Q1. Answer - Dynamic programming is used for problems requiring a sequence of interrelated decision. This means that to take another decision we have to depend on the previous decision or solution formed. dynamic programming is a recursive optimization procedure which means it’s a procedure which optimizes on a step by step basis using information from the preceding steps . We optimize as we go. In dynamic programming , a single step is sequentially related to preceding steps and is not itself a solution to the problem.A single step contains information that identifies a segment or a part of the optimal solution e.g. time dependent problems, decision making.
- 3. Q2 Answer – 1.Stage – division of sequence of a system into various subparts is called stages 2.State – a specific measurable condition of the system 3. Recursive equation – at every stage in dynamic programming the decision rule is determined by evaluating an objective function called recursive equation. 4.Principle of optimality – it states that an optimal set of decisions rules has the property that regardless of the ith decisions, the remaining decisions must be optimal with respect to the outcome that results from the ithdecision. This means that optimal immediate decision depends only on current state and not how you got there
- 4. Q3. ANSWER- The two basic approaches for solving dynamic programming are:- 1.)Backward recursion- a)it is a schematic representation of a problem involving a sequence of n decisions. b)Then dynamic programming decomposes the problem into a set of n stages of analysis, each stage corresponding to one of the decisions. each stage of analysis is described by a set of elements decision, input state, output state and returns. c)Then notational representation of these element when a backward recursion analysis is used d)Then symbolic representation of n stages of analysis using backward recursion so we can formalize the notation
- 5. The general form of the recursion equation used to compute cumulative return:- cumulative return = direct return + cumulative return through stage from stage through stage i-1
- 6. 2.)Forward recursion – this approach takes a problem decomposed into a sequence of n stages and analyzes the problem starting with the first stage in the sequence, working forward to the last stage it is also known as deterministic probability approach
- 7. Q4. Answer- dynamic programming is a recursive optimization procedure which means that it optimizes on a step by step basis using information from preceding steps even in goal programming optimization occurs step by step but it was iterative rather then recursive that means that each step in goal programming represented a unique solution that was non-optimal.in dynamic programming a single step is sequentially related to preceding steps and is not itself a solution to the problem
- 8. Q5. Answer- Advantages - 1)`the process of breaking down a complex problem into a series of interrelated sub problems often provides insight into the nature of problem 2) Because dynamic programming is an approach to optimization rather than a technique it has flexibility that allows application to other types of mathematical programming problems 3) The computational procedure in dynamic programming allows for a built in form of sensitivity analysis based on state variables and on variables represented by stages 4)Dynamic programming achieves computational savings over complete enumeration.
- 9. Disadvantages – 1.)more expertise is required in solving dynamic programming problem then using other methods 2.)lack of general algorithm like the simplex method. It restricts computer codes necessary for inexpensive and widespread use 3.)the biggest problem is dimensionality. This problems occurs when a particular application is characterized by multiple states. It creates lot of problem for computers capabilities & is time consuming