Showing convexity of a discrete function

In summary, the conversation discusses a function ##f:\mathbb{N}\times\mathbb{N}\to\mathbb{R}_+## that is increasing and convex, and how to show that a function ##g(x):=cf(x)-\max\{c_1\left(f(x)-f(x-e_1)\right),c_2\left(f(x)-f(x-e_2)\right)\}## is also convex. The summary outlines the two cases that need to be proven and mentions using the assumptions and properties of the function to rearrange terms and apply them in the proof. It also brings up the idea of using the supermodularity of ##f## to aid in the proof.
  • #1
TaPaKaH
54
0
Suppose we have a function ##f:\mathbb{N}\times\mathbb{N}\to\mathbb{R}_+## that is
  • increasing: ##f(x+e_i)\geq f(x)## for any ##x\in\mathbb{N}^2## and ##i\in\{1,2\}##;
  • convex: ##f(x+2e_i)-f(x+e_i)\geq f(x+e_i)-f(x)## for any ##x\in\mathbb{N}^2## and ##i\in\{1,2\}##.
How could one show that a function ##g(x):=cf(x)-\max\{c_1\left(f(x)-f(x-e_1)\right),c_2\left(f(x)-f(x-e_2)\right)\}## for ##c=\max\{c_1,c_2\}## (##c_1,c_2\geq0##) is also convex wrt definition above?
 
Physics news on Phys.org
  • #2
Have you tried substituting g into that convexity inequality?
 
  • #3
So far I did case-by-case analysis branching on terms, at which maxima for ##f(x+2e_i)##, ##f(x+e_i)## and ##f(x)## are achieved. Six of eight cases went rather smooth, but two cases are giving me problems.

Case 1:
\begin{cases}
c_1\left[f(x)-f(x-e_1)\right]&\geq c_2\left[f(x)-f(x-e_2)\right]\\
c_1\left[f(x+e_1)-f(x)\right]&\geq c_2\left[f(x+e_1)-f(x+e_1-e_2)\right]\\
c_1\left[f(x+2e_1)-f(x+e_1)\right]&\leq c_2\left[f(x+2e_1)-f(x+2e_1-e_2)\right],
\end{cases}
in which case
\begin{array}{rl}
g(x)&=c f(x)-c_1\left[f(x)-f(x-e_1)\right]\\
g(x+e_1)&=c f(x+e_1)-c_1\left[f(x+e_1)-f(x)\right]\\
g(x+2e_1)&=c f(x+2e_1)-c_2\left[f(x+2e_1)-f(x+2e_1-e_2)\right].
\end{array}
What we want to show is that
\begin{array}{rll}
g(x+2e_1)-2g(x+e_1)+g(x)
&=\begin{matrix}
cf(x+2e_1)-c_2\left[f(x+2e_1)-f(x+2e_1-e_2)\right]\\
-2c f(x+e_1)+2c_1\left[f(x+e_1)-f(x)\right]\\
+cf(x)-c_1\left[f(x)-f(x-e_1)\right]
\end{matrix}
&\geq0
\end{array}
Case 2:
\begin{cases}
c_1\left[f(x)-f(x-e_1)\right]&\geq c_2\left[f(x)-f(x-e_2)\right]\\
c_1\left[f(x+e_1)-f(x)\right]&\leq c_2\left[f(x+e_1)-f(x+e_1-e_2)\right]\\
c_1\left[f(x+2e_1)-f(x+e_1)\right]&\leq c_2\left[f(x+2e_1)-f(x+2e_1-e_2)\right],
\end{cases}
in which case
\begin{array}{rl}
g(x)&=c f(x)-c_1\left[f(x)-f(x-e_1)\right]\\
g(x+e_1)&=c f(x+e_1)-c_2\left[f(x+e_1)-f(x+e_1-e_2)\right]\\
g(x+2e_1)&=c f(x+2e_1)-c_2\left[f(x+2e_1)-f(x+2e_1-e_2)\right].
\end{array}
What we want to show is that
\begin{array}{rll}
g(x+2e_1)-2g(x+e_1)+g(x)
&=\begin{matrix}
cf(x+2e_1)-c_2\left[f(x+2e_1)-f(x+2e_1-e_2)\right]\\
-2c f(x+e_1)+2c_2\left[f(x+e_1)-f(x+e_1-e_2)\right]\\
+cf(x)-c_1\left[f(x)-f(x-e_1)\right]
\end{matrix}
&\geq0
\end{array}
I guess the proof lies in rearranging the terms in a smart way and then applying the assumptions and properties. One can also use that ##f## is also supermodular, that is ##f(x+e_1+e_2)+f(x)\geq f(x+e_1)+f(x+e_2)## for any ##x\in\mathbb{N}^2##.
 

FAQ: Showing convexity of a discrete function

1. How do you define convexity in a discrete function?

Convexity in a discrete function refers to the property of the function where the line segment connecting any two points on the function lies above or on the graph of the function. This can also be described as the function having a non-decreasing slope.

2. What is the significance of showing convexity in a discrete function?

Showing convexity in a discrete function is important because it allows us to determine important properties of the function, such as the existence of a unique global minimum or maximum. It also helps in understanding the behavior of the function and its relationship to other functions.

3. How can you prove convexity in a discrete function?

There are several methods for proving convexity in a discrete function. One method is to use the definition of convexity and show that the line segment connecting any two points on the function lies above or on the graph. Another method is to use the second derivative test, where if the second derivative is positive at all points, then the function is convex.

4. Can a function be both convex and concave?

No, a function cannot be both convex and concave. These are opposite properties, where convexity refers to a non-decreasing slope and concavity refers to a non-increasing slope. A function can be either convex or concave, but not both at the same time.

5. Are there any real-world applications of showing convexity in a discrete function?

Yes, there are many real-world applications of showing convexity in a discrete function. For example, in economics, convexity is used to model consumer preferences and determine the optimal allocation of resources. In engineering, convexity is used to optimize designs and improve efficiency. It also has applications in statistics, finance, and computer science.

Similar threads

Back
Top