Answer:
1/2 or 0.5
Step-by-step explanation:
Average rate of change = Change in output / Change in input
= ∆y / ∆x
= 4-2 / 9-5
= 2/4
= 1/2
Answer:
73626983
Step-by-step explanation:
there’s the answer DONT CLICK AND LINK ID SOMEONE SAY U CAN DOWNLOAD THE ANSWER THERE THEY WILL TRACK U
Answer:
Check the explanation
Step-by-step explanation:
1) Algorithm for finding the new optimal flux: 1. Let E' be the edges eh E for which f(e)>O, and let G = (V,E). Find in Gi a path Pi from s to u and a path 
, from v to t.
2) [Special case: If , and 
have some edge e in common, then Piu[(u,v)}uPx has a directed cycle containing (u,v). In this instance, the flow along this cycle can be reduced by a single unit without any need to change the size of the overall flow. Return the resulting flow.]
3) Reduce flow by one unit along 
4) Run Ford-Fulkerson with this sterling flow.
Justification and running time: Say the original flow has see F. Lees ignore the special case (4 After step (3) Of the elgorithuk we have a legal flaw that satisfies the new capacity constraint and has see F-1. Step (4). FOrd-Fueerson, then gives us the optimal flow under the new cePacie co mint. However. we know this flow is at most F, end thus Ford-Fulkerson runs for just one iteration. Since each of the steps is linear, the total running time is linear, that is, O(lVl + lEl).
Answer:
First and Second Derivative Tests. First identifies critical points. The second determines behavior around the point.
- Concave up is a minimum
- Concave down is a maximum
Step-by-step explanation:
The extreme points of a function are called the maximum and/or minimums. AT these points, the function (or y-values) are at their highest or lowest. These points are often the peaks and valleys of a function on a graph. You can determine if a function has max or min using the first and second derivative tests. The first determines critical points of the function. The second determines behavior around a point. If the value is positive then the function is concave up. It forms a valley and the point is a minimum. If the value is negative then the function is concave down. It forms a peak which has a maximum.
