Calculating Dual Form of SVM Equation

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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. A key point of confusion arises regarding the indices for alphas, specifically whether to consider cases where i equals j or when they are different. The user suggests that if i and j are equal, the result simplifies to squared terms, while for i not equal to j, they contemplate duplicating the vector and flipping it. Additionally, there is a clarification regarding the summation notation, indicating that the double summation notation (##\sum_{i,j}##) likely represents a nested summation (##\sum_i\sum_j##), leading to a comprehensive expansion of the products of alphas across all combinations.
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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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