Convolution of e^{-|x|}: What is the result?

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In summary, the conversation discusses proving the convolution of e^{-\left|x\right|} as (1-x)e^{x} for x<0 and (1+x)e^{-x} for x>0. The attempt at a solution involves plugging through the integral and taking limits, but the result keeps coming out to be zero. The conversation suggests carefully examining the absolute values on each interval and showing the work.
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Homework Statement


Prove that the convolution of [itex]e^{-\left|x\right|}[/itex] is [itex](1-x)e^{x}[/itex] for x<0 and [itex](1+x)e^{-x}[/itex] for x>0

Homework Equations





The Attempt at a Solution



I plug through the integral in the standard way and take the limits as x tends to positive and negative infinity etc. But, I keep getting that the convolution is zero?

Any help would be greatly appreciated.
 
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  • #2
Unredeemed said:

Homework Statement


Prove that the convolution of [itex]e^{-\left|x\right|}[/itex] is [itex](1-x)e^{x}[/itex] for x<0 and [itex](1+x)e^{-x}[/itex] for x>0

Homework Equations


The Attempt at a Solution



I plug through the integral in the standard way and take the limits as x tends to positive and negative infinity etc. But, I keep getting that the convolution is zero?

Any help would be greatly appreciated.

You're doing f*f, right? You don't need to take limits in x. You just need to write down the integral and carefully work out what the absolute values are on each interval. Show your work. How did you get 0?
 
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FAQ: Convolution of e^{-|x|}: What is the result?

What is the formula for convolution of exp(-mod(x))?

The formula for convolution of exp(-mod(x)) is:f(x) * g(x) = ∫ f(t) * g(x-t) dt from -∞ to +∞, where f(x) = exp(-mod(x)) and g(x) = exp(-mod(x)).

What is the significance of convolution of exp(-mod(x)) in mathematics?

Convolution of exp(-mod(x)) is significant in mathematics as it represents the blending of two functions. It is a fundamental operation in signal processing, probability theory, and linear systems theory. It also has important applications in image processing and filtering.

What is the graphical representation of convolution of exp(-mod(x))?

The graphical representation of convolution of exp(-mod(x)) is a smooth and symmetric curve that resembles a bell-shaped curve. It is also known as a Gaussian curve or normal distribution.

What are the properties of convolution of exp(-mod(x))?

The properties of convolution of exp(-mod(x)) include commutativity, associativity, distributivity, and shift invariance. It also has a property of being a linear transformation, which means that the convolution of a linear combination of functions is equal to the same linear combination of their convolutions.

What are the applications of convolution of exp(-mod(x))?

Convolution of exp(-mod(x)) has various applications in different fields such as image processing, audio and video signal processing, probability and statistics, and differential equations. It is used to smooth signals, filter out noise, and solve differential equations in engineering, physics, and biology. It is also used in machine learning and artificial intelligence for feature extraction and pattern recognition.

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