Simplex method - Big M - Example 1

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In this video, you will learn how to solve linear programming problems using the big M method. Also, in this example, you will learn how to find out an alternate optimal solution.
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i knew nothing before i watch this video, now i dont even know what i didnt know this is one hardass topic

cevat
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Thanks for this video, it makes the topic easier to understand

modupesodeinde
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U could make it more simple after your first iteration remove artificial variable column calculations will be easy

akshaymanchanda
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thank you very much sir the video was very helpful, I would be pleased if u uploaded a video on Queueing theory

harsharaju
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in the 2nd table while finding out the key row we have 5, -3 and 12. why don't youtake -3. as 5 is more and -3 is less.

winniegoyal
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Slack variable kaha se aata hai oh toh bata hi nahi rahe hai

balramjaiswal
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Thanks to all for helping me to understand the problems

sushadevi
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Solution of your last 6 min is completely wrong

niteshpratapsingh
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not a good choice of question. you should have chosen the question that contains at least two or more artificial variables. this way, the concept would be more clear.

bipinkhatiwada
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you chose right hand sides of the constraints in such a way that you get rid of the A1 in the very first iteration. This is a bad example, leading students to no where in case the key column is something other than that.

saifuddinabdullah
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Why didn't you include s3 in basis variables..

sagkoenigin
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can anyone tell me why we need to choose minimum non negative ratio?

upasanabiswas
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why the basis is s1, s2, a1 but not s3

shaheenalam
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video was helpful but hey!! you are wasting time on explaining unnecessary points. just be straight to the point, like in explaining your steps in solutions don't go off the question becoz you end confusing the viewers.

gordennyabunze
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Watch at speed 1.5x so u don't get deviated

satyanarayanaswamysunkara
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I don't understand why you need A1

SanghoOh
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i hope i can see you dr i will kiss u hhhh thanks for explanation i was lost

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