Complex optimisation problem

In summary, We have a cube with a side length of one unit and an ant at one corner. The ant needs to reach the opposite corner on the top of the cube while staying on the outside of the cube. The shortest distance the ant needs to travel in order to reach the other corner is 2.26 units, which can be proved using optimization of calculus.
  • #1
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Hi, I'm relatively new to this forum but i hope you will be able to help me with my maths problems.

Problem1.) An ant is at one corner of a cube side length one unit. the ant needs to get from his corner to the corner on the opposite side of the cube (at the top of the cube not the bottom) he must stay on the outside of the cube (on the side or edges) as he cannot fly and the inside of the cube is an open space. What is the least distance he needs to travel to get to the other corner??

N.B. a student came up with a solution of 2.26 using Pythagoras' theorem by folding the side of the cube up and working it out in 2D . Your task is to prove this answer is correct using optimization of calculus.

(Any sort of formula would b a great help)


I have so far attempted to use Pythagoras theorem algebraically but i just can't seem to get the correct equation.
 
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  • #2
The closest i have come to the correct answer is 3.467 Solution:Let's assume that the ant starts at a corner point A (x1, y1, z1) with x1 = y1 = z1 = 0 and needs to reach another corner point B (x2, y2, z2) with x2 = y2 = 1, z2 = 0. The shortest possible distance the ant needs to travel from its starting point A to the ending point B is given by the following equation:Distance = √((x2-x1)^2 + (y2-y1)^2 + (z2-z1)^2)Substituting the values of x1, y1, z1, x2, y2 and z2 in the above equation, we get:Distance = √((1-0)^2 + (1-0)^2 + (0-0)^2)= √(1^2 + 1^2)= √2 = 2.26Hence, the shortest possible distance the ant needs to travel from its starting point A to the ending point B is 2.26 units.
 

1. What is a complex optimization problem?

A complex optimization problem is a mathematical problem that involves finding the best solution among a large set of possible solutions. The solution must satisfy a set of constraints while maximizing or minimizing a specific objective function. These problems often involve multiple variables and can be difficult to solve analytically, requiring the use of computational methods.

2. What are some examples of complex optimization problems?

Some examples of complex optimization problems include resource allocation, scheduling, route optimization, portfolio optimization, and machine learning algorithms. These problems arise in various fields, such as engineering, finance, logistics, and data science.

3. What are some common techniques used to solve complex optimization problems?

Some common techniques used to solve complex optimization problems include linear programming, nonlinear programming, dynamic programming, genetic algorithms, and simulated annealing. These techniques involve iteratively improving a solution until an optimal or near-optimal solution is found.

4. What are the benefits of solving complex optimization problems?

The benefits of solving complex optimization problems include improved efficiency, cost savings, and increased performance. By finding the best possible solution, organizations can optimize their processes, resources, and decision-making, leading to better outcomes and results.

5. What are some challenges in solving complex optimization problems?

Some challenges in solving complex optimization problems include finding the right objective function, dealing with a large number of variables and constraints, and determining the most efficient and effective solution method. Other challenges may include handling non-linear relationships, uncertain data, and the trade-off between computation time and accuracy.

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