algorithm design.pptx

ACTIVITY
Choose 5 random numbers
from 1-9.
The numbers should not be
arranged consecutively.

1.What are the classifications of design approaches?
2.What are the classifications of other classifications?
3.Where does this bubble sorting fall into?
4.Why do you think it is sorting algorithm?
5.How does this bubble sorting helps you in solving?
6.Why is it important to know bubble sorting method?

What is computational thinking?
What are the elements of
computational thinking?
What is algorithm?

CLASSROOM NORMS
Treat everyone with respect.
Listen to others.
Do your best to be successful.

123, ARRANGE ME!
Arrange the procedures
properly. Use numbers
(1,2,3,4,5, …) only.

123, ARRANGE ME!
 Group 1: Washing clothes
 Group 2: Fixing a broken table
 Group 3: Going to school
 Group 4: Brushing your teeth
 Group 5: Washing dishes

123, ARRANGE ME!
 What are the procedures you arranged?
 Does following procedures necessary?
 What does following procedures do in our daily
life?
 What do you think is our topic for today?

LEARNING OBJECTIVES
At the end of the lesson the students are able to:
1.Determine and explain the classifications of algorithm
design;
2.Illustrate the categories of implementation method and
design method through venn diagram, fishbone, diagram,
table, and example algorithms; and
3. Appreciate the importance of solving problems in our
daily activities.

1. The class will be divided into 5 (five) groups.
2.You will identify the classifications of Implementation Method and
Design Method.
3.Group 1: make a Venn Diagram of Design Method
4.Group 2: fill in the Table of description in Implementation Method
5.Group 3: make a fishbone diagram of Design Method
6.Group 4: give example algorithms of Implementation Method
7.Group 5: explain the importance of Implementation Method and
Design Method.
8.The classifications will be shown via PowerPoint Presentation.
9.10 minutes to do the activity.
10.Presentation of the output will follow.

GREEDY METHOD
At each step, decision is made to
choose the local optimum, without
thinking about the future consequences
Example: Fractional Knapsack and
Activity Selection

BRANCH AND BOUND
 This technique is very useful in solving combinatorial
optimization problem that have multiple solutions and
we are interested in find the most optimum solution. In
this approach, the entire solution space is represented
in the form of a state space tree.
 Example: Job sequencing and Travelling salesman
problem.

RECURSION OR ITERATION
Recursive algorithm is an algorithm which calls
itself again and again until a base condition is
achieved.
Iterative algorithm use loops and/or data
structures like stacks, qeues to solv e any
problem.
Every recursive solution can be implemented as
an iterative solution and vice versa.

SERIAL OR PARALLEL OR DISTRIBUTED
ALGORITHMS
Serial algorithms, one instruction is executed at
a time.
Parallel algorithms are those in which we divide
the problem into subproblems and execute
them on different processors.
Distributed algorithms are parallel algorithms
that are distributed on different machines.

DYNAMIC PROGRAMMING
 This strategy is similar to divide and conquer, the
difference is that whenever we have recursive
function calls with the same result, instead of calling
them again we try to store the result in a data
structure in the form of a table and retrieve the
results from the table.
 Example: 0-1Knapsack and subset-sum problem

LINEAR PROGRAMMING
there are inequalities in terms of inputs
and maximizing or minimizing some linear
functions of inputs.
Example: Maximum flow or Directed
Graph

REDUCTION (TRANSFORM ANG CONQUER)
In this method, we solve a difficult problem
by transforming it into a known problem for
which we have an optimal solution.
Basically, the goal is to find a reducing
algorithm whose complexity is not
dominated by the resulting reduced
algorithms.

DIVIDE AND CONQUER
This strategy involves dividing the
problem into sub-problem, recursively
solving them, ang then recombining
them for the final answer.
Example: Merge sort and Quicksort

BACKTRACKING
 This technique is very useful in solving
combinatorial problems that have a single
unique solution. Where we have to find the
correct combination of steps that lead to
fulfilment of the task.
 Example: N-queens problem

EXACT OR APPROXIMATE
Exact algorithm are algorithms that are capable
of finding an optimal solution for any problem
are known.
Approximate algorithms are the type of
algorithms that find the result as an average
outcome of sub outcomes to a problem.

BASED ON THE ACTIVITY…
How did you find the activity?
What did you feel while doing the activity?
How did you come up with the result of your activity?
What is the importance of teamwork/cooperation in
group activity?
What are the topics shown on your activity?
What is algorithm?

What do you think are the classifications for Implementation
Method?
What are the classifications for Design method?
Which of the following classifications greatly impacts you as
a student?
Which of these classifications can you apply in your daily
life?
How can we apply algorithm in our daily life?
Why is it important to follow procedures?

WHAT IS ALGORITHM?
 a procedure to solve a
particular problem in a finite
number of steps for a finite-
sized input.

CLASSIFICATIONS OF ALGORITHMS:
1.Implementation Method
2.Design Method
3.Design Approaches
4.Other Classifications

IMPORTANCE OF ALGORITHMS:
 Organization
 Problem solving
 Performance comparison
 Reusability
 Research

ORGANIZATION
Algorithms can be very complex and by
classifying them, it becomes easier to
organize, understand, and compare different
algorithms.

PROBLEM SOLVING
Different problems require different
algorithms, and by having a classification, it
can help identify the best algorithm for a
particular problem.

PERFORMANCE COMPARISON
By classifying algorithms, it is possible to
compare their performance in terms of time
and space complexity, making it easier to
choose the best algorithm for a particular
use case.

REUSABILITY
By classifying algorithms, it becomes
easier to re-use existing algorithms for
similar problems, thereby reducing
development time and improving efficiency.

RESEARCH
Classifying algorithms is essential for
research and development in computer
science, as it helps to identify new
algorithms and improve existing ones.

IMPLEMENTATION METHOD:
1. Recursion or Iteration
2. Exact or Approximate
3. Serial or Parallel or
Distributed Algorithms

RECURSION OR ITERATION
 Recursive algorithm is an algorithm which
calls itself again and again until a base
condition is achieved.
 Iterative algorithm use loops and/or data
structures like stacks, qeues to solv e any
problem.
 Every recursive solution can be implemented
as an iterative solution and vice versa.

EXACT OR APPROXIMATE
 Exact algorithm are algorithms that are
capable of finding an optimal solution for any
problem are known.
 Approximate algorithms are the type of
algorithms that find the result as an average
outcome of sub outcomes to a problem.

DESIGN METHOD
1. Greedy Method
2. Divide and Conquer
3. Dynamic Programming
4. Linear Programming
5. Reduction (Transform and Conquer)
6. Backtracking
7. Branch and Bound

GREEDY METHOD
At each step, decision is made to choose
the local optimum, without thinking
about the future consequences
Example: Fractional Knapsack and
Activity Selection

DYNAMIC PROGRAMMING
 This strategy is similar to divide and conquer, the difference
is that whenever we have recursive function calls with the
same result, instead of calling them again we try to store
the result in a data structure in the form of a table and
retrieve the results from the table.
 Thus, the overall time complexity is reduced. “Dynamic”
means we dynamically decide, whether to call a function or
retrieve values from the table.
 Example: 0-1Knapsack and subset-sum problem

LINEAR PROGRAMMING
there are inequalities in terms of inputs
and maximizing or minimizing some
linear functions of inputs.
Example: Maximum flow or Directed
Graph

REDUCTION (TRANSFORM ANG CONQUER)
 In this method, we solve a difficult problem by transforming
it into a known problem for which we have an optimal
solution. Basically, the goal is to find a reducing algorithm
whose complexity is not dominated by the resulting
reduced algorithms.
 Example: Selection algorithm for finding the median in a
list involves first sorting the list and then finding out the
middle element in the sorted list. These techniques are
also called transform and conquer.

BACKTRACKING
 This technique is very useful in solving combinatorial problems that
have a single unique solution. Where we have to find the correct
combination of steps that lead to fulfillment of the task. Such problems
have multiple stages and there are multiple options at each stage.
 This approach is based on exploring each available option at every
stage one-by-one. While exploring an option if a point is reached that
doesn’t seem to lead to the solution, the program control backtracks one
step, and starts exploring the next option. In this way, the program
explores all possible course of actions and finds the route that leads to
the solution.
 Example: N-queens problem

BRANCH AND BOUND
 This technique is very useful in solving combinatorial
optimization problem that have multiple solutions and we are
interested in find the most optimum solution. In this
approach, the entire solution space is represented in the
form of a state space tree. As the program progresses each
state combination is explored, and the previous solution is
replaced by new one if it is not the optimal than the current
solution.
 Example: Job sequencing and Travelling salesman problem.

FORMATIVE PRACTICE
Based on your understanding, arrange the
classroom norms according to the most
important to the least.
1.Treat everyone with respect.
2.Listen to others.
3.Do you best to be successful.

Based on what you have learned, choose an
activity and create a procedure for:
1.Making a breakfast
2.Doing Laundry
3. Planning Your Day
4. Packing for a Trip

RUBRIC FOR THE ACTIVITY
5
(Exceeds
Expectation)
4
(Meets
Expectation)
3
(Below
Expectations)
1
(Not
Acceptable)
Appropriate use
of words
So clear and
complete
Correct words Some notations
are incorrect
and has
misspelled
words
Incorrect words
Flow of process
presented
So simple and
clear. The
design is
understandable
Clear and
complete
Incomplete or
not well
designed
Unclear or
poorly designed

Directions: Read the questions carefully
and write the letter only. Do this in your
activity notebook.
EVALUATION

1.Which design method involvesbreaking a
problem into smaller sub-problems that can be
solved recursively?
A.Divide and Conquer
B.Dynamic Programming
C.Backtraking
D.Greedy

2. Which design method involves building solutions
incrementally by making decisions at each step that
optimize the overall objective?
C.Backtraking
D.Greedy

3. Which design method involves exploring all
possible solutions to a problem by constructing
a tree of possibilities?
C.Backtraking
D.Greedy

4. Which design method involves breaking a
problem into smaller sub-problems and storing
solutions to avoid repeated calculations?
C.Backtraking
D.Greedy

5. Which design method is used for finding the
shortest path between nodes in a graph?
C.Backtraking
D.Greedy

6. What classification of implementation
method calls itself again and again until a
base condition is achieved?
A.Exact or approximate
B.Recursion or iteration
C.Serial or parallel
D.Branch and bound

7. What algorithm that allows one
instruction is executed at a time?
A.Distributed
B.Parallel
C.Serial
D.Recursion

8. What algorithm divides the problem into
subproblems and execute them on different
processors?
A.Distributed
B.Parallel
C.Serial
D.Recursion

9. What classification of implementation
method that algorithms are capable of finding
an optimal solution for any problem.
A.Exact
B.Recursion
C.Serial
D.Parallel

10. What algorithm is it if the parallel
algorithms are distributed on different
machines?
A.Distributed
B.Parallel
C.Serial
D.Recursion

IDENTIFY THE CLASSIFICATIONS OF THE FOLLOWING:
1.Design Approaches
2.Other Classifications

DIGITAL COMMUNICATIONS
Cloud
Local
Hybrid

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