Convex Optimization Without Slater Condition

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In convex optimization, the Slater condition ensures that the dual optimum equals the primal optimum, but its absence leads to a dual gap. When dealing with nonlinear nonconvex optimization, convexification methods are employed to transform constraints into a convex form. Despite the presence of a dual gap in convex optimization, it is still possible to determine the primal optimum value. The discussion highlights the importance of understanding these concepts for effective optimization. Further insights can be found in the linked article.
mertcan
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Hi, initially I am aware of the fact that when slater condition holds, then dual optimum equals primal optimum in convex optimization. But if slater condition does not hold then dual gap exist. When we have nonlinear nonconvex optimization we apply convexification of constraints including different methods. Actually we have to use convex optimization whereas we have nonlinear nonconvex optimization. So, Even we have some dual gap in our convex optimization how we can find the primal optimum value?
 
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Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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