Well lets look at it lets find multiples that equal 200 when multiplied first to get rid of extra work
so i just took a couple of guesses and lined them up
i got really close when i did this one
-200/30 = -6.666666667
30 + 6.666666667 = 23.33333333
see not quite there almost 25 but not far enough
so ill try to move the numbers around little by little.
-200/29.5 = -6.779661017
29.5 + -6.779661017 = 22.72033898
okay honestly after i finished this trial i wasn't sure if i was thinking straight because i completely didnt think about which way i needed to go so now i pinched myself and this time we might head the right way lol
-200/30.5 = -6.557377049
30.5 + -6.557377049 = 23.94262295
so obviously we need to move up alittle more
-200/31 = -6.451612903
31 + -6.451612903 = 24.5483871
HEY LOOK WE ARE ALMOST THERE!! lets try some more;)
-200/31.5 = -6.349206349
31.5 + -6.349206349 = 25.15079365
see we are at 25! but lets try to get rid of as many numbers behind it as possible.
-200/31.4 = -6.369426752
31.4 + -6.369426752 = 25.03057325
getting closer...
-200/31.39 = -6.371455878
31.39 + -6.371455878 = 25.01854412
what does this mean? this means we can go lower... sooo here we go
-200/31.38 = -6.373486297
31.38 + -6.373486297 = 25.0065137
i guess we can try lower..
-200/31.37 = -6.375518011
31.37 + -6.375518011 = 24.99448199
wait! before you leave! try 31.379
-200/31.379 = -6.37368941
31.379 + <span>-6.37368941 = 25.00531059
</span>okay so yes, i am taking the very long way but good comes to those with patience:D lets try making the third place after the decimal a little bit lower
-200/31.377 = -6.374095675
31.377 + -6.374095675 = 25.00290432
yes we can take it lower so lets make it the best answer
-200/31.376 = -6.374298827
31.376 + <span>-6.374298827 = 25.00170117
</span>i pray this be over soon^ lets try again....
-200/31.375 = -6.374501992
31.375 + -<span>6.374501992 = 25.00049801
</span>okay im a perfectionist... sooo lets go lower
-200/31.374 = -6.37470517
31.374 + <span>-6.37470517 = 24.99929483
</span>okay so we went to low on the third place. and of course i am moving to the fourth place...
-200/31.3749 = -6.374522309
31.3749 + <span>-6.374522309 = 25.00037769
</span>okay we can move lower on that place....
-200/31.3748 = -6.374542627
31.3748 + <span>-6.374542627 = 25.00025737
</span>lets try to move this along faster...
-200/31.3746 = -6.374583262
31.3746 +<span> -6.374583262 = 25.00001674
</span>every number counts literally so moving lower
-200/31.3744 = -6374623897
31.3744 + <span>-6374623897 = 24.9997761
we gotta put a 9 on our fifth place from our decimal sooo here we go again
-200/31.37449 = -6.374605611
31.37449 + </span><span>-6.374605611 = 24.9998439
<span>OMG DO YOU UNDERSTAND WHAT THIS MEANS!!?!?!
</span>okay that means our answer was two trials ago solved
in other words our two numbers arreee
</span><span> 31.3746
</span><span>-6.374583262
</span>hallelujah we are finished and if this is wrong i will pull my hair out.
Answer:
a. Convex solutions ,GO Methods
b. market efficiency
Explanation :
Step-by-step explanation:
A globally optimal solution is one where there are no other feasible solutions with better objective function values. A locally optimal solution is one where there are no other feasible solutions "in the vicinity" with better objective function values. You can picture this as a point at the top of a "peak" or at the bottom of a "valley" which may be formed by the objective function and/or the constraints -- but there may be a higher peak or a deeper valley far away from the current point.
In convex optimization problems, a locally optimal solution is also globally optimal. These include LP problems; QP problems where the objective is positive definite (if minimizing; negative definite if maximizing); and NLP problems where the objective is a convex function (if minimizing; concave if maximizing) and the constraints form a convex set. But many nonlinear problems are non-convex and are likely to have multiple locally optimal solutions, as in the chart below. (Click the chart to see a full-size image.) These problems are intrinsically very difficult to solve; and the time required to solve these problems to increases rapidly with the number of variables and constraints.
GO Methods
Multistart methods are a popular way to seek globally optimal solutions with the aid of a "classical" smooth nonlinear solver (that by itself finds only locally optimal solutions). The basic idea here is to automatically start the nonlinear Solver from randomly selected starting points, reaching different locally optimal solutions, then select the best of these as the proposed globally optimal solution. Multistart methods have a limited guarantee that (given certain assumptions about the problem) they will "converge in probability" to a globally optimal solution. This means that as the number of runs of the nonlinear Solver increases, the probability that the globally optimal solution has been found also increases towards 100%.
Where Multistart methods rely on random sampling of starting points, Continuous Branch and Bound methods are designed to systematically subdivide the feasible region into successively smaller subregions, and find locally optimal solutions in each subregion. The best of the locally optimally solutions is proposed as the globally optimal solution. Continuous Branch and Bound methods have a theoretical guarantee of convergence to the globally optimal solution, but this guarantee usually cannot be realized in a reasonable amount of computing time, for problems of more than a small number of variables. Hence many Continuous Branch and Bound methods also use some kind of random or statistical sampling to improve performance.
Genetic Algorithms, Tabu Search and Scatter Search are designed to find "good" solutions to nonsmooth optimization problems, but they can also be applied to smooth nonlinear problems to seek a globally optimal solution. They are often effective at finding better solutions than a "classic" smooth nonlinear solver alone, but they usually take much more computing time, and they offer no guarantees of convergence, or tests for having reached the globally optimal solution.
Answer:
The odds of choosing a club from a full deck is 13/52, or 1/4. Since there are now only 51 cards and 12 clubs, the odds are 12/51, or .2352
It looks like you had some of the numbers correct, but you just labeled it and rounded it incorrectly, and the teacher gave you super partial credit for it.
For my work, I'm assuming that side A is opposite of angle A and side B is opposite of angle B, etc...
