ALGORITHMS

📌 ALGORITHMS – MINI STUDY GUIDE (IT2212)

1⃣ What is an Algorithm?

According to your handout (page 1):

In simple terms:

An algorithm is just a step-by-step recipe to solve a problem.

Think of it like:

• Cooking instructions 🍳

• Google Maps directions 🗺

• Workout routine 🏋

If the steps are clear and finite (may ending), that’s an algorithm.

🔎 Example from the Handout: Finding the Smallest Number

Set: {3, 5, 6, 1, 2, 7}

What did the algorithm do?

• Compare 3 and 5 → keep 3

• Compare 3 and 6 → keep 3

• Compare 3 and 1 → keep 1

• Compare 1 and 2 → keep 1

• Compare 1 and 7 → keep 1

Final Answer: 1

✨ Notice something?
It keeps a temporary "current smallest" value and updates it.

That’s how most minimum-finding algorithms work.

🧠 Key Characteristics of an Algorithm

From page 2 (Rosen, 2019)

🎯 Example Connection

If I say:
“Keep subtracting 1 forever.”

Is that an algorithm?

❌ NO — it fails Finiteness (never stops).

🧠 Memory Trick:

I Owe DC FEG

I – Input
O – Output
D – Definiteness
C – Correctness
F – Finiteness
E – Effectiveness
G – Generality

2⃣ PSEUDOCODE

From page 1

Pseudocode is like fake code that looks like programming but isn’t tied to any language.

Why use it?
Because:

• Python looks different from Java

• Java looks different from C++

So instead of writing real code, we write something universal.

Example from the Handout

procedure min(a1, a2, a3, …, an : integers)
min := a1
for i := 2 to n
    if min > ai then
        min := ai
return min

Let’s break it down:

• min := a1 → assume first element is smallest

• loop from 2 to n

• compare each element

• update min if needed

✨ This part is important kasi ito ‘yung foundation ng searching algorithms.

3⃣ FLOWCHARTS

From page 1–2

A flowchart is a visual representation of an algorithm.

It’s read:
➡ Top to bottom
➡ Left to right

🔷 Common Flowchart Symbols

Real-life analogy:

Flowchart = Waze navigation

Start → Decision (Traffic?) → Choose route → Arrive

4⃣ CONTROL STRUCTURES

This is SUPER IMPORTANT for exams.

Control structures tell the algorithm:

• What to execute

• When to execute

• How many times to execute

From page 2–3

There are TWO major types:

A. Conditional Controls (Decision-Making)

Used when we need choices.

1⃣ if-then

If x ≥ 60 then
    Output "Passed"

Only runs if condition is true.

2⃣ if-then-else

If x ≥ 60 then
    Output "Passed"
else
    Output "Failed"

This one gives two possible outcomes.

Real-life example:

If may pera ka → order food

Else → tubig muna 😭

B. Loop Controls (Repetition)

Used when we repeat steps.

1⃣ for-do Loop

Used when we know how many times to repeat.

Example from page 3 (finding minimum)

For i = 2 to n do
    If xi < min then
        min ← xi

Used for counting loops.

2⃣ while-do Loop

Used when repetition depends on condition.

Example (page 3): Dividing by 2 repeatedly

While n is even do
    n ← n/2

Repeats while condition is true.

3⃣ repeat-until Loop

Runs first, checks later.

Repeat
    Output i
Until i = 0

Key difference:

• while → checks before running

• repeat → runs first, checks after

🔥 Quick Comparison Table

Memory Trick:

FWR

F – Fixed (for loop)

W – Wait (while checks first)

R – Run first (repeat runs first)

5⃣ TRACING AN ALGORITHM

From page 3–4

Tracing = manually running the algorithm step-by-step.

Why do we trace?

• To check correctness

• To find errors

• To understand variable changes

Example (Pass/Fail)

Input: 75
75 ≥ 60 → YES → Passed

Input: 50
50 ≥ 60 → NO → Failed

Example (Finding Minimum)

Set: {5, 4, 8, 3}

Step-by-step:

• min = 5

• compare with 4 → min = 4

• compare with 8 → still 4

• compare with 3 → min = 3

Final answer: 3

Tracing shows how variables change.

💡 COMMON EXAM CONFUSIONS

❓ Algorithm vs Pseudocode

Algorithm = concept
Pseudocode = how we write it

❓ While vs Repeat

While → might not run at all
Repeat → runs at least once

❓ Why must algorithms be finite?

Because infinite loops = useless program.

🧠 FINAL RECAP (Memory Hacks Mode)

If you remember NOTHING else, remember this:

1⃣ Algorithm = Finite + Clear + Stops

Think: Recipe with ending.

2⃣ 7 Properties → “I Owe DC FEG”

Input
Output
Definiteness
Correctness
Finiteness
Effectiveness
Generality

3⃣ Control Structures = CD + Loops

Conditional → if, if-else
Loops → for, while, repeat

4⃣ Loop Memory Trick → FWR

For = Fixed count
While = Wait/check first
Repeat = Run first

5⃣ Tracing = Manual simulation

If you can trace it, you understand it.

🧠 30-Second Master Summary

An algorithm is a finite, precise set of steps that solves a problem.
It must have input, output, correctness, and must stop.
We write it using pseudocode and visualize it using flowcharts.
Control structures (if, loops) control decision-making and repetition.
Tracing helps test if the algorithm works properly.