


trigonometric identities solver
The term algebra states the constants and variables in the equation. Algebra is one of the familiar topics in mathematics. Algebraic fraction is similar to the usual fraction but a difference is that the numerator and the denominator are algebraic expressions. Algebraic fraction simplifying is similar to that of algebraic fractions. Simplifying algebraic expressions is very easy and simple. In online, students can learn about algebraic expressions. In online, algebraic calculator is helpful to solve the given problem. In online calculator, when the denominator and numerator value is entered, calculator automatically simplifies the problem.
Example : Simplifying Algebraic Fractions
Simplify 3b/9b2.
Solution:
The given problem is an algebraic expression.,
3b = 3*b
9b2 9*b*b
Like terms gets cancelled,
On simplifying this,
Answer = 1/3b
Need More help with Algebra 1
libname k "c:\book";
proc gplot data = k.help (where=((female eq 1)
and (substance eq "alcohol")));
bubble cesd*age=i1 / bscale = radius bsize=60
bcolor=blue bfill=solid;
run;
load(url("http://www.math.smith.edu/sasr/datasets/savedfile"))
femalealc = subset(ds, female==1 & substance=="alcohol")
with(femalealc, symbols(age, cesd, circles=i1,
inches=1/5, bg="blue"))
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Telegraph ::
IIT cut-off raises brows
CHARU
The concerns were dismissed by a top IIT-JEE official as “tilting at windmills”. But the fact that the aggregate cut-off did not change much in 2010, suggesting that the difficulty level of questions was similar to previous years’, offers legitimacy to these concerns.
The IIT-JEE this year was ravaged by errors in instructions given in question papers. One of the major errors involved instructions, which erroneously switched the labelling on physics and math answer scripts, which are read by an optical mark reader (OMR) machine.
Instructors at all test centres announced the error once it was recognised, but many students had already started marking answer scripts by that time — and these answer scripts were not replaced with fresh ones. Amid fears that many students might have marked physics answers where they were to mark math answers and math instead of physics for no fault of theirs, the IITs announced a “corrective measure”.
The OMR machine would mark answer sheets meant for physics (or math) for both subjects and award students the higher ! score. This way, the institutes argued, a student who followed! the ins tructions and marked correct physics (or math) answers on the math (or physics) answer sheet would not lose out.
But in an affidavit before
A handful of such questions — where an incorrect answer matched the correct answer of the other subject — would suffice to clear the average cut-off in that subject, he argued.
Kumar’s concerns have now triggered a debate within the IITs and among IIT-JEE observers over whether his arguments need a relook, following ! the hike in subject cut-offs announced by the IITs on Saturday! .
The IITs first shortlist students who clear subject cut-offs in each of the three subjects tested — physics, chemistry and math. An aggregate cut-off of total scores across the three subjects is set next, based on the total number of seats available to finalise selected candidates.
The subject cut-offs in physics, chemistry and math this year are 19, 19 and 17 respectively as opposed to 8, 11 and 11 in these subjects last year, according to data released on Saturday by the IITs. The sam! e formula — the average marks of all students in a subject is set as the subject cut-off — was followed both in 2009 and 2010.
The hike could normally be explained by easier questions — or better average performance — this year. But such a scenario would also mean much higher aggregate scores of students on average — 55 as opposed to 30 last year. This, in turn, would translate into a correspondingly higher aggregate cut-off.
The aggregate cut-off in the 2010 JEE, according to the IITs, was 19! 0 as opposed to 178 last year — a difference statistical! ly much smaller than the variation between aggregate averages in 2009 and this year. In other words, while the average scores of students improved, the toppers performed almost similar to their counterparts last year — suggesting the question papers were overall not much easier in 2010 compared with 2009.
Some candidates scoring higher than they deserved, because of the corrective measure, would offer a possible explanation for the hike in subject marks without a corresponding jump in the performance of qualified candidates.
Times of
IIT JEE average mark almost doubles but question remains
Akshay Mukul, TNN,
Is the increase in cut-off — calculated on the basis of average performance of candidates — a case of improved standards or is it due to the corrective measures applied by IITs to neutralise errors in the JEE question paper as pointed out by TOI?
In order to paper over its mistakes, IIT-JEE, for the first time, evaluated each answer sheet in two ways — sequential question numberwise and subject headingwise. The higher score of the two systems was taken as a candidate’s score. This strategy wa! s adopted to offset the interchange of subject headings of mathematics and physics in question paper and Optical Response Sheet. IIT is not disclosing the marks scored by both ways of evaluation. Now, IIT insiders are pointing out faults with this method.
Normally, IITs evaluate the JEE question paper by the sequential method. A senior faculty member of IIT-Delhi said, “Sequencewise evaluation is how evaluation should be done. But subject ! heading-wise evaluation leads to going through mathematics ans! wers on the basis of instructions which was for physics. A similar thing happened in case of physics answers. The second way of evaluation may give higher score to candidates who performed poorly.” JEE is a traditionally low-scoring examination.
The cut-off for general category candidates this year is 190, a slight improvement from last year’s 178. However, this year’s average marks for mathematics, physics and chemistry is 17, 19 and 19 respectively, making the average marks 55 out of 489. The same procedure was applied for deciding cut-off in 2009 and 2010. If we compare the average marks of 2009 and 2010, it is clear that cut-off of physics this year is almost 2.4 times last year’s cut-off. Mathematics cut-off is 1.5 times more than last year.
This is much higher than the subject average of 2009 that was 11 (mathematics), 8 (physics) and 11 (chemistry). The average marks last year was 29 out of 489. Thus, the overall performance in 2010 is 1.9 times better than last year. The marginal increa! se in cut-off — 178 to 190 — is indicative that th! e perfor mance of the selected candidates has marginally improved. However, the overall performance is almost double. “A clearer picture,” a source said, “will emerge only when IITs reveal both ways of evaluation.”
Now, here is the trick. If you are struggling with the concept of a word or other problem that deals with units, change the way that you are looking at it, to focus on the units involved. They will guide you towards the final solution.
Naturally, it's best to try to understand the concepts of the questions, and the more complex questions won't be quite so easy to muddle through just by tracking the units. However, hopefully you can see the benefit of looking at these types of problems from this angle, and it will help you to solve them. I learned this technique in high school, and I still benefit from it. As always, feel free to drop me a line if you would like me to clear anything up. :)
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Transportation Method
A transportation tableau is given below. Each cell represents a shipping route (which is an arc on the network and a decision variable in the LP formulation), and the unit shipping costs are given in an upper right hand box in the cell.
To solve the transportation problem by its special purpose algorithm, the sum of the supplies at the origins must equal the sum of the demands at the destinations (Balanced transportation problem).
• If the total supply! is greater than the total demand, a dummy destination is added with demand equal to the excess supply, and shipping costs from all origins are zero.
• Similarly, if total supply is less than total demand, a dummy origin is added. The supply at the dummy origin is equal to the difference of the total supply and the total demand. The costs associated with the dummy origin are equal to zero.
When solving a transportation problem by its special purpose algorithm, unacceptable shipping routes are given a cost of +M (a large number).
Develop an Initial Solution
Two methods to get the initial solution:
• Northwest Corner Rule
• Minimum Cell-Cost Method
Northwest Corner Rule
1. Starting from the northwest corner of the transportation tableau, allocate as much quantity as possible to cell (1,1) from Origin 1 to Destination 1, within the supply constraint of source 1 and the demand! constraint of destination 1.
2. The first allocation ! will sat isfy either the supply capacity of Source 1 or the destination requirement of Destination 1.
Æ' If the demand requirement for destination 1 is satisfied but the supply capacity for Source 1 is not exhausted, move on to cell (1,2) for next allocation.
Æ' If the demand requirement for destination 1 is not satisfied but the supply capacity for Source 1 is exhausted, move to cell (2,1)
Æ' If the demand requirement for Destination 1 is satisfied and the supply capacity for Source 1 is also exhausted, move on to cell (2,2).
3. Continue the allocation in the same manner toward the southeast corner of the transportation tableau until the supply capacities of all sources are exhausted and the demands of all destinations are satisfied.
Initial tableau developed using Northwest Corner Method
Total Cost = 12(400)+13(100)+4(700)+9(100)+12(200)+4(500)= 142,000
Minimum Cell-Cost Method
Although the North-west Corner Rule is the easiest, it is not the most attractive because our objective is not included in the process.
Steps of Minimum Cell-Cost Method
1. Select the cell with the minimum cell cost in the tableau and allocate as much to this cell as possible, but within the supply and demand constraints.
2. Select the cell with the next minimum cell-cost and allocate as much to this cell as possible within the demand a! nd supply constraints.
3. Continue the procedure until! all of the supply and demand requirements are satisfied. In a case of tied minimum cell-costs between two or more cells, the tie can be broken by selecting the cell that can accommodate the greater quantity.
Initial tableau developed using Minimum Cell-Cost Method
Total Cost = 12(300)+4(200)+4(700)+10(100)+9(200)+4(500)= 120,000
MODI Method (for obtaining reduced costs)
Associate a number, ui, with each row and vj with each column.
• Step 1: Set u1 = 0.
•! ; Step 2: Calculate the remaining ui's and vj's by solving the relationship cij
= ui + vj for occupied cells.
• Step 3: For unoccupied cells (i,j), the reduced cost = cij - ui - vj.
Step 1: For each unoccupied cell, calculate the reduced cost by the MODI method. Select the unoccupied cell with the most negative reduced cost. (For maximization problems select the unoccupied cell with the largest reduced cost.) If none, STOP.
Step 2: For this unoccupied cell, gen! erate a stepping stone path by forming a closed loop with this! cell an d occupied cells by drawing connecting alternating horizontal and vertical lines between them. Determine the minimum allocation where a subtraction is to be made along this path.
Step 3: Add this allocation to all cells where additions are to be made, and subtract this allocation to all cells where subtractions are to be made along the stepping stone path. (Note: An occupied cell on the stepping stone path now
becomes 0 (unoccupied). If more than one cell becomes 0, make only one unoccupied; make the others occupied with 0's.)
GO TO STEP 1.
Example: Acme Block Co. (ABC)
Acme Block Company has orders for 80 tons of concrete blocks at three suburban locations as follows: Northwood -- 25 tons, Westwood -- 45 tons, and Eastwood -- 10 tons. Acme has two plants, each of which can produce 50 tons per week. Delivery cost per ton from each plant to each suburban location is shown below.
How should end of week shipments be made to fill the above orders?
Since total supply = 100 and total demand = 80, a dummy destination is created with demand of 20 and 0 unit costs.
Iteration 1: Tie for least cost (0), arbitrarily select x14. Allocate 20. Reduce s1 by 20 to 30 and delete the Dummy column.
Iteration 2: Of the remaining cells the least cost is 24 for x11. Allocate 25. Reduce s1 by 25 to 5 and eliminate the Northwood column.
Iteration 3: Of the remaining cells the least cost is 30 for x12. Allocate 5. Reduce the Westwood column to 40 and eliminate the Plant 1 row. Iteration 4: Since there is only one row with two cells left, make the final allocations of 40 and 10 to x22 and x23, respectively.
1. Set u1 = 0
2. Since u1 + vj = c1j for occupied cells in row 1, then v1 = 24, v2 = 30, v4 = 0.
3. Since ui + v2 = ci2 for occupied cells in column 2, then u2 + 30 = 40, hence u2 = 10.
4. Since u2 + vj = c2j for occupied cells in row 2, then 10 + v3 = 42, hence v3 = 32.
Calculate the reduced costs (circled numbers on the p! revious slide) by cij - ui - vj.
Unoccupied Cell Reduced Cost (1,3) 40 - 0 - 32 = 8 (2,1) 30 - 24 -10 = -4 (2,4) 0 - 10 - 0 = -10
Iteration 1:
The stepping stone path for cell (2,4) is (2,4), (1,4), (1,2), (2,2). The allocations in the subtraction cells are 20 and 40, respectively. The minimum is 20, and hence reallocate 20 along this path. Thus for the next tableau:
x24 = 0 + 20 = 20 (0 is its current allocation) x14 = 20 - 20 = 0 (blank for the next tableau) x12 = 5 + 20 = 25
x22 = 40 - 20 = 20
The other occupied cells remain the same.
1. Set u1 = 0.
2. Since u1 + vj = cij for occupied cells in row 1, then v1 = 24, v2 = 30.
3. Since ui + v2 = ci2 for occupied cells in column 2, then u2 + 30 = 40, or u2 =
10.
4. Since u2 + vj = c2j for occupied cells in row 2, then 10 + v3 = 42 or v3 = 32;
and, 10 + v4 = 0 or v4 = -10.
Iteration 2
Calculate the reduced costs (circled numbers on the previous slide) by cij - ui - vj.
Unoccupied Cell Reduced Cost (1,3) 40 - 0 - 32 = 8 (1,4) 0 - 0 - (-10) = 10 (2,1) 30 - 10 - 24 = -4
The most negative reduced cost is = -4 determined by x21. The stepping stone path for this cell is (2,1),(1,1),(1,2),(2,2). The allocations in the subtraction cells are 25 and 20 respectively. Thus the new solution is obtained by reallocating 20 on the stepping stone path. Thus for the next tableau:
x21 = 0 + 20 = 20 (0 is its current allocation)
x11 = 25 - 20 = 5
x12 = 25 + 20 = 45
!x22 = 20 - 20 = 0 (blank for the next tableau)
!The o ther occupied cells remain the same.
1. Set u1 = 0
2. Since u1 + vj = c1j for occupied cells in row 1, then v1 = 24 and v2 = 30.
3. Since ui + v1 = ci1 for occupied cells in column 2, then u2 + 24 = 30 or u2 =6.
4. Since u2 + vj = c2j for occupied cells in row 2, then 6 + v3 = 42 or v3 = 36, and 6 + v4 = 0 or v4 = -6.
Iteration 3
Calculate the reduced costs (circled numbers on the previous slide) by cij - ui - vj.
Unoccupied Cell Reduced Cost
(1,3) | 40 - 0 - 36 = | 4 |
(1,4) | 0 - 0 - (-6) = | 6 |
(2,2) | 40 - 6 - 30 = | 4 |
Since all the reduced costs are non-negative, this is the optimal tableau.