9 votes

Non-symmetric Positive Definite/Semidefinite Matrix in Quadratic Program

Yes, a real PSD matrix $M$ is a symmetric matrix with $$x^TMx\ge 0$$ for any $x$ (see e.g. https://en.wikipedia.org/wiki/Definite_matrix). However, this is not a real restriction. (We have two ...
Erwin Kalvelagen's user avatar
5 votes

Augmented Lagrangian Function for Semidefinite Programming Problems

My way of reading it is $\langle X, \mathcal{A}^*(y)+S-C\rangle = \langle X, \mathcal{A}^*(y)-C\rangle + \langle X,S \rangle$. The first term is your standard inner product between dual variable and ...
Johan Löfberg's user avatar
4 votes

Augmented Lagrangian Function for Semidefinite Programming Problems

There's a good discussion of this in Convex Optimization by Stephen Boyd and Lieven Vandenberghe. See section 5.9. With an ordinary scalar inequality constraint: $f_{i}(x) \leq 0$, you'll have a term ...
Brian Borchers's user avatar
3 votes

Conditions required for strong duality to hold for SDPs

The following is from Section 2.2 of Semidefinite Programming for Combinatorial Optimization by Christoph Helmberg. Let's define \begin{align*} p^* &= \inf\{\langle C,X\rangle\,:\,X\text{ primal ...
Thomas Kalinowski's user avatar
1 vote

Solver for nonlinear semidefinite optimization

If you use Python, I have found calling the scs solver through cvxpy to be very effective for semidefinite programming. The <...
Robert Bassett's user avatar

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