Multilinear Maps of V^n into Reals and Effect of Linear Transforma

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In summary, the conversation is about finding a way to show that a multilinear map, M, and a linear map, L, both have strictly-positive values when certain conditions are met. The discussion revolves around using tensors and determinants to prove this, or finding a more rigorous approach involving the preservation of orientation by L.
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WWGD
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Hi, All:

Is there a "nice" , non-messy way of showing this:

Let [itex]M(v_1,v_2,..,v_n) → R+[/itex], where R is the Reals, be a multilinear map,

where [itex] v_i[/itex] are vectors in a finite-dimensional vector space V.

Now, let [itex] L: Vn → Vn [/itex] be a linear map with Det(L)>0 .

How do we show that [itex]M(L(v_1,v_2,..,v_n))([/itex] is also strictly-positive? I think it

has to see with the fact that the map L preserves the orientation of [itex]Vn[/itex],

but I don't see how to make this more rigorous. Any ideas?
 
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  • #2
I would write ##M,L## as tensors and search whether there are some nice formulas which connect tensors and determinantes, or otherwise go the hard way by coordinates.
 

1. What is a multilinear map?

A multilinear map is a mathematical function that takes multiple inputs and returns a single output. In the context of "Multilinear Maps of V^n into Reals and Effect of Linear Transforma," the multilinear map takes in a vector of n-dimensional real numbers and outputs a real number.

2. How are multilinear maps used in scientific research?

Multilinear maps have many applications in scientific research, particularly in fields such as linear algebra, geometry, and quantum computing. They can be used to represent and manipulate data in high-dimensional spaces, which is useful in many scientific disciplines.

3. What is the significance of linear transformations in multilinear maps?

Linear transformations are important in multilinear maps because they preserve the linearity of the map. This means that the output of the map will still be a linear combination of the input vectors, even after a linear transformation is applied. This property allows for easier analysis and manipulation of the multilinear map.

4. How do multilinear maps impact machine learning?

Multilinear maps have been used in machine learning to model complex data and relationships between variables. They can also be used to create more efficient and accurate algorithms for tasks such as image recognition and natural language processing.

5. What are some challenges associated with multilinear maps?

One challenge with multilinear maps is their computational complexity, as they require a significant amount of time and resources to compute. There are also security concerns surrounding multilinear maps, as they have been used in cryptographic protocols and have been subject to attacks by hackers.

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