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Any force, however small, can go against gravity.
The minimum speed for a ball rolling down an incline occurs near the top. Gravity will speed the ball up as it travels down.
The weight of an object will be minimum at high point like mountain. This is because weight depends on acceleration due to gravity and acceleration due to gravity is inversely proportional to the square of distance from center of earth to the object. As high places like mountain is far from earth center, there is minimun weight. But at the center of earth the weight becomes null as gravity pull takes place in all direction from the center.
A ball rolls down hill because there is no force preventing it from moving. If there was an object in the way, that would be the force that counters the movement/roll.
Keeping the aircraft as light as possible. Having the centre of gravity as far back(aft) as the flight manual will allow. Flying at the minimum drag speed.
Since there is no feasible region defined, there is no answer possible.
On the poles the gravity will be maximum. on the equatorial region the gravity will be minimum
The answer depends on what the feasible region is and on what operator is between 6x and 8y.
It is 18.
The answer depends on the feasible region and there is no information on which to determine that.
2x+2y
If we knew the values of 'x' and 'y', and the boundaries of the feasible region, we could answer that question quickly and easily.
It is 18.
Any force, however small, can go against gravity.
It is usually the answer in linear programming. The objective of linear programming is to find the optimum solution (maximum or minimum) of an objective function under a number of linear constraints. The constraints should generate a feasible region: a region in which all the constraints are satisfied. The optimal feasible solution is a solution that lies in this region and also optimises the obective function.
0 m/s (no motion)
It is usually the answer in linear programming. The objective of linear programming is to find the optimum solution (maximum or minimum) of an objective function under a number of linear constraints. The constraints should generate a feasible region: a region in which all the constraints are satisfied. The optimal feasible solution is a solution that lies in this region and also optimises the obective function.