Answer:
20 miles
Step-by-step explanation:
Let x = amount charge each mile
y = total price paid
Since the Uber charged $1.10 for each mile, we multiply x by 1.1 like this 1.1x
Also, $2 were already charged so we add 2 to the total price
y = 1.1x + 2
Since the Lyft charged $0.95 for each mile, we multiply x by 0.95 like this 0.95x
Also, $5 were arleady charged so we add 5 to the total price
y = 0.95x + 5
We have to find a price where both rides match
1.1x + 2 = 0.95x + 5
Let's move the like terms to one side by subtracting 0.95x from both sides
1.1x + 2 = 0.95x + 5
-0.95x -0.95x
0.15x + 2 = 5
Now, subtract 2 from both sides
0.15x + 2 = 5
- 2 - 2
0.15x = 3
Finally, divide both sides by the coefficient, 0.15, to isolate the variable, x
0.15x/0.15 = 3/0.15
x = 20
This means they traveled 20 miles if they were to pay the same price
The tree is 7.5ft because it's shadow is half it's height.
Answer:
The slope of line A is C. 2
Step-by-step explanation:
You can check the slope by dividing the rise (how many units up) and the run (how many units across). This line has a rise of rise of 4 and a run of 2. Diving 4 by 2 gives you a slope of 2.
It only equals positive 12, that symbol means it equals positive and negative 12
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.
