Answer:
a) Surface area = 616 cm²
b) Volume = V = 1083 cm³
Step-by-step explanation:
Surface area and volume of a regular triangular pyramid that has a base edge of 16 cm and a slant height of 15 cm
a) Formula for surface area = Base area × 1/2(perimeter × slant height)
Base area = Base edge²
=( 16 cm)² = 256 cm²
Perimeter = Base edge × 3
= 16 cm × 3 = 48 cm
Hence:
Surface Area = 256 cm² + 1/2(48 × 15)
= 256 cm² + 360 cm²
= 616 cm²
b) Volume = 1/3 × Base area × Height
1082.758616785cm³
Approximately = 1083 cm³
Answer:
As this question is incomplete, but we will try to solve this question by adding our own data to understand the concept of the problem.
Explanation is given below
Step-by-step explanation:
As this question is incomplete, but we will try to solve this question by adding our own data to understand the concept of the problem.
In order to answer this question we need to have the dimensions of the box.
Let's suppose there are 6 outside surfaces of the box and are equal in dimension including the bottom side which Jan wants to varnish.
So,
Let's suppose,
Surface area of the cube = 6
Here, Surface area = 275 square inch
Surface area of the cube = 6 = 275 square inch
= 275/6 = 45.833
a = 
a = 6.77 inches
Now, for the amount of the varnish, we need the spreading rate of the varnish to be used on the box,
Let's suppose it is = 11 square incher per litre.
So,
Required Varnish = Surface area / Spreading rate
Required varnish = 275 / 11
Required varnish = 25 liters
If the 1 container of varnish contains 25 liters then it will be sufficient to protest the outside surfaces of the box.
It will take 19 years and 6 months for the account value to reach 2900 dollars
Step-by-step explanation:
Given
Principal amount = 1400 dollars
Rate = 5.5 %
Final value = A = 2900
We have to find t
So,
The formula for simple interest is:
Putting values
Dividing both sides by 0.055
Rounding off to nearest tenth
19.5 years
Hence,
It will take 19 years and 6 months for the account value to reach 2900 dollars
Keywords: Interest, simple interest
Learn more about interest at:
#LearnwithBrainly
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.
