Calculating Dual Form of SVM Equation

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SUMMARY

The discussion focuses on calculating the dual form of a Support Vector Machine (SVM) optimization problem. The user is working with vectors of alphas and target outputs, along with a precomputed Kernel matrix. The primary confusion arises regarding the indices of the alphas, specifically whether to use equal indices or distinct ones when summing the products of alpha values. The user concludes that the notation ##\sum_{i,j}## likely represents a double summation over all combinations of indices.

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I am trying to calculate the dual form of an SVM optimisation problem:

ELWm1.png

Dual Form Optimsation Problem

In my algorithm, I have a vector of alphas, vector of target outputs, and a Kernel matrix computing upfront.

However, I am stuck as to what indices alpha and j should be taking here. If i and j are equal, then everything is just squared. Is this for i =/= j?

If so, could I just duplicate each vector and flip it?
 
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I'm going to suspect that the ##\sum_{i,j}## in your equation is being used as a shorthand for ##\sum_i\sum_j##, in which case ##\sum_i\sum_j\alpha_i\alpha_j = \alpha_1\alpha_1 + \alpha_1\alpha_2 + \cdots + \alpha_1\alpha_m + \alpha_2\alpha_1 + \cdots + \alpha_2\alpha_m + \cdots + \alpha_n\alpha_1 + \cdots \alpha_n\alpha_m## if ##i = 1 \ldots n## and ##j = 1 \ldots m##.
 

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