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Optimization with convex constraints and convex objectives; notions related to convex optimization such as sub-gradients, normal cones, separating hyperplanes
3
votes
Does removing some constraints in convex program change the optimal solution?
In convex optimization, a local optimum is a global one. So if you do not change the neighbourhood of the optimal solution, you do not change the optimal solution.
1
vote
Accepted
A difficult combinatorial optimization problem
First of all, one can shift the set $\mathcal{J}$ so that the condition $x_i \leq 1$ can be replaced by $x_i \geq 0$.
Let $M_i$ be the maximal absolute value of $x_i$ in $\mathcal{J}$, then we can i …
2
votes
Fastest 'Oracle' Algorithm for satisfying a single linear constraint on a convex set?
Actually, I guess that they do not mean that such an oracle always exists. Usually, the argument is of the form: If, for your special problem, you have a quick method of checking if this problem has a …