Table of Contents

## What is a non convex function?

A non-convex function is wavy – has some ‘valleys’ (local minima) that aren’t as deep as the overall deepest ‘valley’ (global minimum). Optimization algorithms can get stuck in the local minimum, and it can be hard to tell when this happens.

**What is non convex cost function?**

A convex function: given any two points on the curve there will be no intersection with any other points, for non convex function there will be at least one intersection. In terms of cost function with a convex type you are always guaranteed to have a global minimum, whilst for a non convex only local minima.

### What does non convex mean in math?

A polygon is convex if all the interior angles are less than 180 degrees. If one or more of the interior angles is more than 180 degrees the polygon is non-convex (or concave).

**What is convex function example?**

Convex function on an interval. A function (in black) is convex if and only if the region above its graph (in green) is a convex set. A graph of the bivariate convex function x2 + xy + y2.

## What are convex non convex optimization?

A convex optimization problem is a problem where all of the constraints are convex functions, and the objective is a convex function if minimizing, or a concave function if maximizing. A non-convex optimization problem is any problem where the objective or any of the constraints are non-convex, as pictured below.

**What is convex and non convex function?**

A convex function has one minimum – a nice property, as an optimization algorithm won’t get stuck in a local minimum that isn’t a global minimum. Take x2−1, for example: A non-convex function is wavy – has some ‘valleys’ (local minima) that aren’t as deep as the overall deepest ‘valley’ (global minimum).

### Is cross entropy convex?

The binary cross-entropy being a convex function in the present case, any technique from convex optimization is nonetheless guaranteed to find the global minimum.

**What is a non convex problem?**

A non-convex optimization problem is any problem where the objective or any of the constraints are non-convex, as pictured below. Such a problem may have multiple feasible regions and multiple locally optimal points within each region.

## Is sin x a convex function?

If f”(−1)<0 , then f is concave (commonly called “concave down”) at x=−1 . Since f”(−1)>0 , we see that sinx is convex (“concave up”) at x=−1 .

**What is an example of a convex function?**

Examples in High Dimensions – max function: f(x) = maxixiis convex – Quadratic over linear function: f(x,y)=x2/y is convex for y>0 r2f(x,y)= 2 y3 y2xy xy x2

### Can any function be given a non-convex loss function?

Any function can be given a non-convex loss function. For a given function [math]f[/math], the loss function is simply something that you as modeler decide on. Some choices are better than others, and some have intuitive appeal given the context of the problem.

**What is the difference between convex and quasiconvex?**

For a convex function f , {displaystyle f,} the sublevel sets {x | f(x) < a} and {x | f(x) ≤ a} with a ∈ R are convex sets. However, a function whose sublevel sets are convex sets may fail to be a convex function. A function whose sublevel sets are convex is called a quasiconvex function.

## Can a function be strictly convex and not strongly convex?

Notice that this definition approaches the definition for strict convexity as m → 0, and is identical to the definition of a convex function when m = 0. Despite this, functions exist that are strictly convex but are not strongly convex for any m > 0 (see example below).