Explain Time Complexity & Space Complexity. Why are they important?
1. Time Complexity
Time Complexity batata hai ki ek algorithm ko run hone me kitna time lagta hai, ya phir input size badhne par algorithm ka speed kaise change hota hai.
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Simple shabdon me:
“Algorithm kitni fast ya slow chalti hai?” -
Time complexity ko mostly Big-O notation se represent karte hain
→ O(1), O(n), O(n²), O(log n), etc.
Example:
Agar tumhare algorithm me loop 1 baar chal raha hai → O(1)
Agar loop n times chal raha hai → O(n)
Agar nested loop hai → O(n²)
2. Space Complexity
Space Complexity batata hai ki algorithm ko kitni memory (RAM) ki zarurat hoti hai, input size badhne par memory usage kitna badhta hai.
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Simple shabdon me:
“Algorithm kitni memory use karta hai?” -
Isme memory include hoti hai:
✔ Variables
✔ Data structures (arrays, stacks, queues)
✔ Extra temporary memory
Example:
Agar algorithm sirf 1 variable use kare → O(1)
Agar algorithm array of n items use kare → O(n)
Why are Time & Space Complexity Important?
1. Efficiency check karne ke liye
Complexity se pata chalta hai ki algorithm fast hai ya slow, light hai ya heavy.
2. Large inputs ke liye best algorithm choose karne ke liye
Chhota input sab handle kar leta hai,
par big data me sirf efficient algorithm kaam aata hai.
3. Performance compare karne me helpful
Do algorithms me se kaun better hai —
yeh time & space complexity se decide hota hai.
4. Optimization karne me madad milti hai
Agar algorithm slow ya zyada memory use kare,
to complexity dekh kar usse optimize kiya ja sakta hai.
5. Real-world applications me zaroori
Jahan speed aur memory limited hoti hai,
jaise:
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Mobile apps
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Online servers
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Gaming
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AI systems
Waha efficient algorithm hi chal sakta hai.
Short Exam-Friendly Summary
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Time Complexity → Algorithm ko run hone me lagne wala time.
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Space Complexity → Algorithm ko chalne me lagne wali memory.
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Dono important hain kyunki ye batate hain ki algorithm kitna fast aur kitna memory-efficient hai.
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