MHB How to solve following optimization problem?

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The optimization problem presented is defined as maximizing the expression ax + by^3 under the constraints 0 ≤ x ≤ 1 and 0 ≤ y ≤ 1. This problem is confirmed to be convex since it involves a convex function over a convex set. The KKT method can be applied, but the inequality constraints can be expressed using the constraint function g(x,y) = (-x, -y, x-1, y-1). The maximum value of the function is found to be a + b at the point (1, 1), while the minimum value is 0 at (0, 0). Thus, the analysis concludes that the optimal solution is achieved at the boundary of the defined constraints.
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The following is the mathematical expression for my model's rate expression. Variables $x,y$ are the controlling parameter, while the rest are positive constants.

$$\max_{x,y} \ ax + by^3 \ (s.t. \ 0\leq x \leq 1,\ 0\leq y\leq1)$$

Can I mathematically say that it is a convex problem within the limits of variables $x,y$?

The graph for the equation strictly follows the definition of convexity.
I have learned to solve the problems with KKT method, but I cannot understand how to resolve the inequality constraints $0 \leq (x,y) \leq 1$.
 
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user_01 said:
The following is the mathematical expression for my model's rate expression. Variables $x,y$ are the controlling parameter, while the rest are positive constants.
$$\max_{x,y} \ ax + by^3 \ (s.t. \ 0\leq x \leq 1,\ 0\leq y\leq1)$$
Can I mathematically say that it is a convex problem within the limits of variables $x,y$?
The graph for the equation strictly follows the definition of convexity.

Wiki explains that a convex problem is the optimization of a convex function over a convex set.
So it is indeed a convex problem as both conditions are satisfied.

I have learned to solve the problems with KKT method, but I cannot understand how to resolve the inequality constraints $0 \leq (x,y) \leq 1$.
Those inequality constraints translate into the constraint function $\mathbf g(x,y) = (-x,-y,x-1,y-1)$ with $g_i(x,y) \le 0$.

Btw, we can already see by inspection that the optimum must be at $(x,y)=(1,1)$ with value $a+b$.
 
The derivative of $ax+ by^3$ with respect to x is the constant a. If a is not 0, that derivative is never 0 so any max or min must be on the boundary. That boundary consists of 4 pieces

1) x= 0, $0\le y\le 1$. The function reduces to $by^3$ on $0\le y\le 1$ which obviously has its minimum value, 0, at y= 0 and maximum value, b, at y= 1.
2) y= 0, $0\le x\le 1$. The function reduces to $ax$ on $0\le x\le 1$ which obviouslly has its minimum value, 0, at x= 0 and maximum value , a, a x=1.
3) x= 1, $0\le y\le 1$. The function reduces to $a+ by^3$ on $0\le y\le 1$ which obviouly has its minimum, a, at y= 0 and its maximum a+ b at y= 1.
4. y= 1, $0\le x\le 1$. The function reduces to $ax+ b$ on $0\le x\le 1$ which obviouslly has its minimum, b, at x= 0 and its maximum, a+ b at x= 1.

Over all, then, the maximum is a+ b which is achieved at (1, 1). The minimum value is 0 which is achieved at (0, 0).
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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