Yes, in optimization problems, the feasible region must be a convex set to ensure that the objective function has a unique optimal solution. This is because convex sets have certain properties that guarantee the existence of a single optimum within the feasible region.
A set is said to be convex with respect to the origin if the line segment between any two points in the set lies entirely within the set. In simpler terms, for any two points within the set, all the points on the line joining them are also within the set.
If the production set is convex, it means that any combination of inputs that produces a certain level of output can be formed by a convex combination of other input combinations. This implies that the production function exhibits diminishing returns to scale, leading to concavity. This concavity arises because as more units of an input are added, the incremental increase in output becomes smaller.
Convex refers to a shape or surface that curves outward like the exterior of a circle. In mathematics, it describes a set where any line segment connecting two points within the set lies completely within the set. Convexity is often used in optimization and geometry to simplify problem-solving.
Yes, but it can be hard to arrange. You need to set up a real image as a virtual object, and make the convex mirror image that. If the rays converge strongly enough, they will still converge after reflecting off the convex mirror.
Focused sunlight is very strong, and can set fire to paper, as well as destroying your vision if you were so foolish as to look at the sun through a convex lens. Even without the use of a lens, it is not safe to look directly at the sun.
Yes.
Depending on the context, the "feasible region" or "solution set".
the union of two convex sets need not be a convex set.
The feasible region is one possible anwer to this incomplete question.
no
To find the minimum value of (2x + 2y) in a feasible region, you typically need to know the constraints that define that region. If you have a specific set of inequalities or constraints, you can apply methods like the corner point theorem or linear programming techniques to evaluate the objective function at the vertices of the feasible region. Without specific constraints, it's impossible to determine the minimum value accurately. If you provide the constraints, I can assist you further in finding the minimum.
To find the minimum value of the expression (6x + 10y) in a feasible region, you need to identify the constraints defining that region. Typically, the minimum occurs at one of the vertices of the feasible set formed by these constraints. By evaluating the objective function (6x + 10y) at each vertex, you can determine which one gives the lowest output, thus identifying the minimum value. If you provide the specific constraints, I can help you find the exact minimum.
yes
The answer depends on how it is halved. If the plane is divided in two by a step graph (a zig-zag line) then it will not be a convex set.
A convex polygon is one with no reflex angles (angles that measure more than 180 degrees when viewed from inside the polygon). More generally a convex set is on where a straight line between any two points in the set lies completely within the set.
no, because it should be a segment .
It can be if the set consists of convex shapes, for example.