Solve the LP problem. If no optimal solution exists, indicate whether the feasible region is empty or the objective function is unbounded. HINT [See Example 1.] (Enter EMPTY if the region is empty. Enter UNBOUNDED if the function is unbounded.) Minimize c = x + y subject to x + 2y 2 3 2x + y2 3 x2 0, y 2 0. (x,y) - (
Addition Rule of Probability
It simply refers to the likelihood of an event taking place whenever the occurrence of an event is uncertain. The probability of a single event can be calculated by dividing the number of successful trials of that event by the total number of trials.
Expected Value
When a large number of trials are performed for any random variable ‘X’, the predicted result is most likely the mean of all the outcomes for the random variable and it is known as expected value also known as expectation. The expected value, also known as the expectation, is denoted by: E(X).
Probability Distributions
Understanding probability is necessary to know the probability distributions. In statistics, probability is how the uncertainty of an event is measured. This event can be anything. The most common examples include tossing a coin, rolling a die, or choosing a card. Each of these events has multiple possibilities. Every such possibility is measured with the help of probability. To be more precise, the probability is used for calculating the occurrence of events that may or may not happen. Probability does not give sure results. Unless the probability of any event is 1, the different outcomes may or may not happen in real life, regardless of how less or how more their probability is.
Basic Probability
The simple definition of probability it is a chance of the occurrence of an event. It is defined in numerical form and the probability value is between 0 to 1. The probability value 0 indicates that there is no chance of that event occurring and the probability value 1 indicates that the event will occur. Sum of the probability value must be 1. The probability value is never a negative number. If it happens, then recheck the calculation.
again, do not understand how to complete this type of problem
The given linear programming problem is as follows.
Minimize
Subject to the constraints
The solution of the constraint consists of all points on the line and the half-plane determined by this line satisfying the inequality .
Consider the equation corresponding to the constraint .
When the value , we have .
When the value , we have .
Clearly, the origin does not satisfy the inequality .
Hence, the half-plane satisfying does not contain the origin.
Thus, the solution of the constraint is the set of all points on the line joining the points and along with the points on the half-plane determined by this line that does not contain the origin.
Similarly, the solution of the constraint consists of all points on the line and the half-plane determined by this line satisfying the inequality .
Consider the equation corresponding to the constraint .
When the value , we have .
When the value , we have .
Clearly, the origin does not satisfy the inequality .
Hence, the half-plane satisfying does not contain the origin.
Thus, the solution of the constraint is the set of all points on the line joining the points and along with the points on the half-plane determined by this line that does not contain the origin.
The inequalities and implies that the feasible region lies in the first quadrant.
The intersection of the solutions of these constraints gives the feasible region.
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