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What is perion corner?
Perion Corner is the (only) largest image database for the game Maple Story (Global)
Why are there only 5 regular polyhedra?
understand you need at leas 3 faces per corner to make a 3d object and all shapes on a regular polyhedron must be regular.triangles:tetrahedron,(3 per corner) octohedron,(4 per corner) icosahedron,(5 per corner) there is none with 6per corner because that would be 2d as all shapes must be regularsquares:cube(3 per corner) is the only one because 2 or 4 would both be 2d.pentagons:dodecaahedron(3 per corner) is the only one because the pentagon fits together in no other way.hexagons(non-existant)there are none because the simplest way of maching them(3 per corner) is 2d.
Has a triangle got square corners?
Only if it is a right angle triangle which will have only 1 square corner of 90 degrees
When you graph a line using only the slope and a point what step do you start with?
When you graph a line using only the slope and a point, you start by graphing the point.
What has only has one end point?
A perspective vanishing point on the horizon
Can a linear programming problem have multiple optimal solutions?
When solving linear prog. problems, we base our solutions on assumptions.one of these assumptions is that there is only one optimal solution to the problem.so in short NO. BY HADI It is possible to have more than one optimal solution point in a linear programming model. This may occur when the objective function has the same slope as one its binding constraints.
Is it possible for an linear programming model to have exact two optimal solutions?
Yes, but only if the solution must be integral. There is a segment of a straight line joining the two optimal solutions. Since the two solutions are in the feasible region part of that line must lie inside the convex simplex. Therefore any solution on the straight line joining the two optimal solutions would also be an optimal solution.
Does greedy algorithm always work?
Greedy algorithms are only guaranteed to produce locally optimal solutions within a given time frame; they cannot be guaranteed to find globally optimal solutions. However, since the intent is to find a solution that approximates the global solution within a reasonable time frame, in that sense they will always work. If the intent is to find the optimal solution, they will mostly fail.
Is (-10) a linear inequality?
II. SIMPLEX ALGORITHM A. Primal Simplex Algorithm If the unconstrained solution space is defined in n dimensions (each dimension assumed to be infinite), each inequality constraint in the linear programming formulation divides the solution space into two halves. The convex shape defined in n-dimensional space after m bisections represents the feasible area for the problem, and all points which lie inside this space are feasible solutions to the problem. Figure 1 shows the feasible region for a problem defined in two variables, n = 2, and three constraints, m = 3. Note that in linear programming, there is an implicit non-negativity constraints for the variables. The linearity of the objective function implies that the the optimal solution cannot lie within the interior of the feasible region and must lie at the intersection of at least n constraint boundaries. These intersections are known as corner- point feasible (CPF) solutions. In any linear programming problem with n decision variables, two CPF solutions are said to be adjacent if they share n − 1 common constraint boundaries. When interpreted geometrically, the Simplex algorithm moves from one corner-point feasible solution to a better corner-point-feasible solution along one of the constraint boundaries. There are only a finite number of CPF solutions, although this number is potentially exponential in n, however it is not necessary to visit all of them to determine the optimal solution to the problem. The convex nature of linear programming means that there are no local maxima present in the problem which are not also global maxima. Hence if at some CPF solution, no improvement is made by a move to another adjacent CPF then the algorithm terminates and we can be confident that the optimal solution has been found.
What is primal simplex method?
II. SIMPLEX ALGORITHM A. Primal Simplex Algorithm If the unconstrained solution space is defined in n dimensions (each dimension assumed to be infinite), each inequality constraint in the linear programming formulation divides the solution space into two halves. The convex shape defined in n-dimensional space after m bisections represents the feasible area for the problem, and all points which lie inside this space are feasible solutions to the problem. Figure 1 shows the feasible region for a problem defined in two variables, n = 2, and three constraints, m = 3. Note that in linear programming, there is an implicit non-negativity constraints for the variables. The linearity of the objective function implies that the the optimal solution cannot lie within the interior of the feasible region and must lie at the intersection of at least n constraint boundaries. These intersections are known as corner- point feasible (CPF) solutions. In any linear programming problem with n decision variables, two CPF solutions are said to be adjacent if they share n − 1 common constraint boundaries. When interpreted geometrically, the Simplex algorithm moves from one corner-point feasible solution to a better corner-point-feasible solution along one of the constraint boundaries. There are only a finite number of CPF solutions, although this number is potentially exponential in n, however it is not necessary to visit all of them to determine the optimal solution to the problem. The convex nature of linear programming means that there are no local maxima present in the problem which are not also global maxima. Hence if at some CPF solution, no improvement is made by a move to another adjacent CPF then the algorithm terminates and we can be confident that the optimal solution has been found.
What is the difference between greedy algorithm and Divide and Conquer?
greedy method does not give best solution always.but divide and conquer gives the best optimal solution only(for example:quick sort is the best sort).greedy method gives feasible solutions,they need not be optimal at all.divide and conquer and dynamic programming are techniques.
What does the solution represent when a system of equations has a single solution?
The graphs of the two equations have only one intersection point.
What do you call a corner of a 3D shape?
A vertex (plural: vertices or vertexes) is the point of intersection of lines (like the 'corners' of a 3D shape). A corner is the same but where only two lines are intersecting.
Does every pair of linear simultaneous equations have a unique solution?
So, take the case of two parallel lines, there is no solution at all. Now look at two equations that represent the same line, they have an infinite number of solutions. The solution is unique if and only if there is a single point of intersection. That point is the solution.
What are the advantages of Point Of Sale?
Point of sale typically refers to the software solution that is used at the point of purchase by the sales clerk. The advantages of having a point of sale solution is that it allows the store to simplify the checking process. Not only does point of sale software simplify the entire process but also tracks it. Managers can log in and analyze sales, customers, and trends. By using a point of sale software solution you will simplify your business operations, gather valuable data, and improve your flexibility. In the long run a point of sale solution should save you time, money and headaches.
Why should the rising fluid be below the point of loading the solution in paper chromatography?
The paper chromatography technique is based on ascending process in which the loaded amino acid or carbohydrate rises along with the solvent only when the rising fluid is below the point of loading solution, if it is above the loaded solution then descending process occurs
Four Corners state?
ColoradoUtahArizonaNew Mexico
