hey Mam once i entered the university and discovered your channel i start follow your lessons one by one , i call you my teacher you really helped me a lot much much more than what they teach us in the university great work and lot of benefits from this channel keep up the good work and i am here if in any time you wanted a help in presenting those courses and for free !!! you are a mercy Mam thanks again
00:00 hi students coming to the next topic 00:02 that is a greedy method so when compared 00:05 to all algorithm approach whatever we 00:07 have so far discuss the simplest and 00:09 straightforward approach you call it as 00:11 a greedy method this Brede method is the 00:14 simplest and straightforward 00:22 straightforward approach so actually 00:25 this is not an algorithm just it is just 00:28 simply technique so greedy method is a 00:32 technique you can call it as a technique 00:37 rather than calling as an algorithm it's 00:39 better to call it as a technique so 00:41 actually what this approach will do what 00:45 the main function of this approach 00:46 actually in this approach the decision 00:50 is taken their greedy method the 00:54 decision is taken on basis of current 01:01 available information okay so in the 01:07 greedy method the decision is taken on 01:09 the basis of current available 01:11 information whatever the values that 01:13 whatever the information that is present 01:15 at present so the decision will be taken 01:20 based on that values so and it does not 01:26 know without worrying about worrying 01:33 about the effect of current to decision 01:40 in feature means it doesn't bother about 01:44 the future work so it just discuss what 01:47 is the present work is going on so a 01:49 bream method is a decision the decision 01:52 is taken on the basis of current 01:54 available information without worrying 01:57 without worrying about the effect of 02:00 current decision in future so it doesn't 02:03 think about whether suppose if I insert 02:06 these values it will at present it is 02:09 working but in future it may or may not 02:12 works so it doesn't bother 02:13 about that it think about the present 02:16 decision so this technique is used to 02:19 determine a feasible solution this 02:23 technique is determined to a feasible 02:26 solution that may or may not be optimal 02:30 that may or may not optimal okay so what 02:38 is a feasible solution what is an 02:39 optimal solution actually feasible 02:42 solution is any subset that satisfies 02:45 the given criteria you call it as a 02:46 feasible solution means suppose if you 02:49 take any n put n inputs its solution 02:53 contains a subset of inputs so whatever 02:55 the n inputs you're taking this a 02:57 solution contains a subset of inputs 03:00 which satisfies a given condition so if 03:03 you take any subset any subset that 03:10 satisfy the condition you call it as a 03:18 feasible solution means in all the 03:21 subsets its if any subset is satisfied 03:23 then you call it a that solution that 03:26 subset is a feasible solution what is an 03:28 optimal solution optimal is nothing but 03:31 it takes the best or most favorable 03:33 solution best or most favorable solution 03:39 so here in the feasible solution if any 03:41 subset satisfies the condition you can 03:43 take any one so if all the solutions 03:45 tell me you take you just take any one 03:47 of the solution but in optimal it takes 03:50 only the best and the most favorable 03:52 solution that is present in the 03:54 objective function so it is just like a 03:56 feasible solution but where objective 03:59 function reaches its maximum or minimum 04:01 value then you call it as a optimal 04:05 solution now let us see what are the 04:08 characteristics of Bri method 04:14 characteristics and features of greedy 04:20 method so to construct the solution in 04:24 octave optimal way this algorithm 04:27 maintains two sets so the first point is 04:30 to construct them to construct the 04:35 solution in an optimal optimal way this 04:43 algorithm maintains this algorithm 04:47 maintains two sub two sets so that is 04:52 one contains choosen items one set 04:58 contains choosen items means which are 05:02 having the solution chosen items and the 05:05 next other contains another set contains 05:09 rejected items so whatever you're taking 05:16 the to construct a solution if you want 05:18 to construct an solution by using the 05:20 building method to make it as an optimal 05:23 thing you have to select two sets one 05:26 the selected items and another is a 05:29 rejected items means one gives the best 05:31 and minimum value and another gives a 05:33 maximum value and the second characters 05:37 this greedy algorithm makes good local 05:39 choice in the hope that the results in 05:41 optimal solution and the feasible 05:44 solution so greedy algorithm make good 05:54 local choices means it's shellack the 05:58 choices in the hope they result in a an 06:01 optimal solution optimal solution or a 06:07 feasible solution feasible solution so 06:13 these are the features and 06:15 characteristics now let us see the 06:19 components that are present in 06:22 components of greedy algorithm 06:26 so in this video I am just giving the 06:28 overview of greedy algorithm what this 06:31 greedy algorithm contains what it mainly 06:33 bases on so it's just selects resolution 06:36 that the things about the present not 06:38 about the future it takes the best value 06:41 that is related to the present value so 06:46 next the components that are present in 06:48 the brady alga rhythm is first as a 06:50 candidate set it candidate set so 06:57 candidates that means here a solution is 06:59 created based on this set so whatever 07:03 the solution that the greedy method is 07:04 creating that is a solution is created 07:07 by using the candidate set from this set 07:12 and the second point is and the second 07:18 component is a selection function so 07:22 what's the use of selection function in 07:24 the greedy algorithm so this selection 07:26 function is used to choose the best 07:31 candidate the best candidate to be added 07:39 to the solution means you are selecting 07:43 the best subset and next third one the 07:48 third component that is present is a 07:50 feasible functional if feasibility 07:54 function so what is the use of this 08:00 feasibility function it is used to 08:03 determine whether a candidate can be 08:13 used to contribute the solution so 08:17 whether the candidate is used to 08:19 contribute the solution is not is 08:21 decided by the feasibility function and 08:24 coming to the next the fourth component 08:26 that is present in the greedy algorithm 08:29 is a objective function objective 08:34 function so what is the use of this 08:36 objective function actually this is user 08:38 to 08:40 to function is used to assign a value 08:43 just it has science a value to a 08:47 solution or a partial solution so 08:50 whether it is a partial solution or the 08:52 solution so this objective function is 08:54 used to assign a value and the next 08:58 component that is is a solution function 09:01 the final is a solution function so what 09:05 is the use of the solution function and 09:07 the greedy algorithm it is used to it is 09:13 used to indicate indicate whether a 09:20 complete solution has been reached or 09:26 not so it's just a solution function is 09:30 final decision it is used to indicate 09:32 whether a complete solution has been 09:34 reached or not that will be decided by 09:36 the solution function so these are the 09:38 five components that are present in the 09:40 video algorithm it candidates it a 09:42 selection function a feasibility 09:43 function objective function and a 09:46 solution function now coming to the 09:49 applications that are used in the greedy 09:50 algorithm what are the areas of 09:52 application so let me write that areas 09:58 of applications so in which fills it's 10:06 greedy method is used actually the 10:07 greedy method is used to solve many 10:09 problems so what type of problems it is 10:12 used to solve so it is used to solve the 10:15 shortest path finding shortest path 10:20 finding shortest path and it is used to 10:25 finding the minimum spanning tree 10:28 finding minimum spanning tree by using 10:31 the prims algorithm or the kruskal's 10:32 algorithm and the next application is 10:35 job sequencing job sequencing with 10:42 deadlines 10:44 and fractional knapsack problems 10:48 fractional knapsack problems so these 10:55 are the different applications of the 10:57 greedy method so in this greedy method 11:02 topic we'll discuss about each and every 11:04 application clearly so let me explain 11:06 the pseudocode of the greedy algorithm 11:08 so after that we will continue with the 11:10 applications so pseudocode pseudocode 11:18 for greedy algorithm writing here 11:27 already them greedy so a comma and a is 11:34 an array and n is the number of inputs 11:36 that we are giving so let us take the 11:40 solution a solution is equal to 0 means 11:43 we are just initializing the solution to 11:45 0 now checking fir I easy I is equal to 11:51 1 to n do X should be select a means we 12:01 are selecting the array elements or 12:03 whatever the elements that are present 12:04 in that so that elements we have to 12:06 select it and that should be assigned to 12:09 X value and we have to check if the 12:12 feasible solution is present or not 12:14 feasible solution comma X sorry right 12:18 solution solution comma X so this is 12:22 starting the solution should be 0 and 12:24 the item the first select element in the 12:27 array so if feasible solution comma X 12:31 then you have to combine that the 12:34 solution is equal to Union of solution 12:40 comma X finally you have to return the 12:44 solution written solution 12:49 okay so this is the pseudocode for the 12:52 greedy algorithm so in the next video 12:54 we'll discuss about the each and every 12:56 application finding shortest path 12:58 finding minimum spanning tree job 12:59 sequencing with the deadline and 13:01 fractional knapsack problems thank
@@HarikaVlogs20 I am happy that this helped you and I will try to share the Google Drive link of the notes I have on this. I am hopeful that it will help you all a lot.🤗
Mam ur videos are very helpful for my studies.actually i am not even getting clg notes.so i prefer ur lecture notes..so thanku very much..😍😍plz continue this work.it will be very helpful for the students like me..
hey Mam once i entered the university and discovered your channel i start follow your lessons one by one , i call you my teacher you really helped me a lot
much much more than what they teach us in the university
great work and lot of benefits from this channel keep up the good work
and i am here if in any time you wanted a help in presenting those courses
and for free !!!
you are a mercy Mam thanks again
same bro......exam me mam hi bchaari h mujhe nhi toh fail hojau
00:00 hi students coming to the next topic
00:02 that is a greedy method so when compared
00:05 to all algorithm approach whatever we
00:07 have so far discuss the simplest and
00:09 straightforward approach you call it as
00:11 a greedy method this Brede method is the
00:14 simplest and straightforward
00:22 straightforward approach so actually
00:25 this is not an algorithm just it is just
00:28 simply technique so greedy method is a
00:32 technique you can call it as a technique
00:37 rather than calling as an algorithm it's
00:39 better to call it as a technique so
00:41 actually what this approach will do what
00:45 the main function of this approach
00:46 actually in this approach the decision
00:50 is taken their greedy method the
00:54 decision is taken on basis of current
01:01 available information okay so in the
01:07 greedy method the decision is taken on
01:09 the basis of current available
01:11 information whatever the values that
01:13 whatever the information that is present
01:15 at present so the decision will be taken
01:20 based on that values so and it does not
01:26 know without worrying about worrying
01:33 about the effect of current to decision
01:40 in feature means it doesn't bother about
01:44 the future work so it just discuss what
01:47 is the present work is going on so a
01:49 bream method is a decision the decision
01:52 is taken on the basis of current
01:54 available information without worrying
01:57 without worrying about the effect of
02:00 current decision in future so it doesn't
02:03 think about whether suppose if I insert
02:06 these values it will at present it is
02:09 working but in future it may or may not
02:12 works so it doesn't bother
02:13 about that it think about the present
02:16 decision so this technique is used to
02:19 determine a feasible solution this
02:23 technique is determined to a feasible
02:26 solution that may or may not be optimal
02:30 that may or may not optimal okay so what
02:38 is a feasible solution what is an
02:39 optimal solution actually feasible
02:42 solution is any subset that satisfies
02:45 the given criteria you call it as a
02:46 feasible solution means suppose if you
02:49 take any n put n inputs its solution
02:53 contains a subset of inputs so whatever
02:55 the n inputs you're taking this a
02:57 solution contains a subset of inputs
03:00 which satisfies a given condition so if
03:03 you take any subset any subset that
03:10 satisfy the condition you call it as a
03:18 feasible solution means in all the
03:21 subsets its if any subset is satisfied
03:23 then you call it a that solution that
03:26 subset is a feasible solution what is an
03:28 optimal solution optimal is nothing but
03:31 it takes the best or most favorable
03:33 solution best or most favorable solution
03:39 so here in the feasible solution if any
03:41 subset satisfies the condition you can
03:43 take any one so if all the solutions
03:45 tell me you take you just take any one
03:47 of the solution but in optimal it takes
03:50 only the best and the most favorable
03:52 solution that is present in the
03:54 objective function so it is just like a
03:56 feasible solution but where objective
03:59 function reaches its maximum or minimum
04:01 value then you call it as a optimal
04:05 solution now let us see what are the
04:08 characteristics of Bri method
04:14 characteristics and features of greedy
04:20 method so to construct the solution in
04:24 octave optimal way this algorithm
04:27 maintains two sets so the first point is
04:30 to construct them to construct the
04:35 solution in an optimal optimal way this
04:43 algorithm maintains this algorithm
04:47 maintains two sub two sets so that is
04:52 one contains choosen items one set
04:58 contains choosen items means which are
05:02 having the solution chosen items and the
05:05 next other contains another set contains
05:09 rejected items so whatever you're taking
05:16 the to construct a solution if you want
05:18 to construct an solution by using the
05:20 building method to make it as an optimal
05:23 thing you have to select two sets one
05:26 the selected items and another is a
05:29 rejected items means one gives the best
05:31 and minimum value and another gives a
05:33 maximum value and the second characters
05:37 this greedy algorithm makes good local
05:39 choice in the hope that the results in
05:41 optimal solution and the feasible
05:44 solution so greedy algorithm make good
05:54 local choices means it's shellack the
05:58 choices in the hope they result in a an
06:01 optimal solution optimal solution or a
06:07 feasible solution feasible solution so
06:13 these are the features and
06:15 characteristics now let us see the
06:19 components that are present in
06:22 components of greedy algorithm
06:26 so in this video I am just giving the
06:28 overview of greedy algorithm what this
06:31 greedy algorithm contains what it mainly
06:33 bases on so it's just selects resolution
06:36 that the things about the present not
06:38 about the future it takes the best value
06:41 that is related to the present value so
06:46 next the components that are present in
06:48 the brady alga rhythm is first as a
06:50 candidate set it candidate set so
06:57 candidates that means here a solution is
06:59 created based on this set so whatever
07:03 the solution that the greedy method is
07:04 creating that is a solution is created
07:07 by using the candidate set from this set
07:12 and the second point is and the second
07:18 component is a selection function so
07:22 what's the use of selection function in
07:24 the greedy algorithm so this selection
07:26 function is used to choose the best
07:31 candidate the best candidate to be added
07:39 to the solution means you are selecting
07:43 the best subset and next third one the
07:48 third component that is present is a
07:50 feasible functional if feasibility
07:54 function so what is the use of this
08:00 feasibility function it is used to
08:03 determine whether a candidate can be
08:13 used to contribute the solution so
08:17 whether the candidate is used to
08:19 contribute the solution is not is
08:21 decided by the feasibility function and
08:24 coming to the next the fourth component
08:26 that is present in the greedy algorithm
08:29 is a objective function objective
08:34 function so what is the use of this
08:36 objective function actually this is user
08:38 to
08:40 to function is used to assign a value
08:43 just it has science a value to a
08:47 solution or a partial solution so
08:50 whether it is a partial solution or the
08:52 solution so this objective function is
08:54 used to assign a value and the next
08:58 component that is is a solution function
09:01 the final is a solution function so what
09:05 is the use of the solution function and
09:07 the greedy algorithm it is used to it is
09:13 used to indicate indicate whether a
09:20 complete solution has been reached or
09:26 not so it's just a solution function is
09:30 final decision it is used to indicate
09:32 whether a complete solution has been
09:34 reached or not that will be decided by
09:36 the solution function so these are the
09:38 five components that are present in the
09:40 video algorithm it candidates it a
09:42 selection function a feasibility
09:43 function objective function and a
09:46 solution function now coming to the
09:49 applications that are used in the greedy
09:50 algorithm what are the areas of
09:52 application so let me write that areas
09:58 of applications so in which fills it's
10:06 greedy method is used actually the
10:07 greedy method is used to solve many
10:09 problems so what type of problems it is
10:12 used to solve so it is used to solve the
10:15 shortest path finding shortest path
10:20 finding shortest path and it is used to
10:25 finding the minimum spanning tree
10:28 finding minimum spanning tree by using
10:31 the prims algorithm or the kruskal's
10:32 algorithm and the next application is
10:35 job sequencing job sequencing with
10:42 deadlines
10:44 and fractional knapsack problems
10:48 fractional knapsack problems so these
10:55 are the different applications of the
10:57 greedy method so in this greedy method
11:02 topic we'll discuss about each and every
11:04 application clearly so let me explain
11:06 the pseudocode of the greedy algorithm
11:08 so after that we will continue with the
11:10 applications so pseudocode pseudocode
11:18 for greedy algorithm writing here
11:27 already them greedy so a comma and a is
11:34 an array and n is the number of inputs
11:36 that we are giving so let us take the
11:40 solution a solution is equal to 0 means
11:43 we are just initializing the solution to
11:45 0 now checking fir I easy I is equal to
11:51 1 to n do X should be select a means we
12:01 are selecting the array elements or
12:03 whatever the elements that are present
12:04 in that so that elements we have to
12:06 select it and that should be assigned to
12:09 X value and we have to check if the
12:12 feasible solution is present or not
12:14 feasible solution comma X sorry right
12:18 solution solution comma X so this is
12:22 starting the solution should be 0 and
12:24 the item the first select element in the
12:27 array so if feasible solution comma X
12:31 then you have to combine that the
12:34 solution is equal to Union of solution
12:40 comma X finally you have to return the
12:44 solution written solution
12:49 okay so this is the pseudocode for the
12:52 greedy algorithm so in the next video
12:54 we'll discuss about the each and every
12:56 application finding shortest path
12:58 finding minimum spanning tree job
12:59 sequencing with the deadline and
13:01 fractional knapsack problems thank
❤
@@HarikaVlogs20 I am happy that this helped you and I will try to share the Google Drive link of the notes I have on this. I am hopeful that it will help you all a lot.🤗
Bane extral mingutunnav ga@@AmitKumar77794
Explain the general structure of a greedy algorithm. What are the key components that make an algorithm “greedy”?
Really ur notes is so clear and easy to understand
Your content is very very important before the exam day❤
Mam ur videos are very helpful for my studies.actually i am not even getting clg notes.so i prefer ur lecture notes..so thanku very much..😍😍plz continue this work.it will be very helpful for the students like me..
hi
Super video mam.i understand very well
i love ur channel so much thank youuuu a lot
From Tutorialpoints...but Nice Xplanation
This helped me very much ,thank you
very helpful
Ma'am some of your videos are private.. plz public them
where can i get some solved problems based on greedy algorithm ?
Where is the connected graph Tutorial.Kindly share
hatsoff
can I get ur notes
Can I have Hamiltonian Cycle topic?
how to see your private video in this topic ie lecture 3,15,21,36,37,44,48,55
Greedy method algorithm aa technique aa clarity ga cheppandi
Hi mam i am pavan kumar
Mam in our college the DAA class in not going on can u pls tell me this for pls mam
Can you send ur notes to my Mali