This work presents a generalization of the well-known homogeneous self-dual embedding model, which is widely used in conic optimization, and uses the Douglas-Rachford algorithm to find a solution and discusses its efficient implementation by exploiting the problem structure.
Abstract
We present a generalization of the well-known homogeneous self-dual embedding model, which is widely used in conic optimization. The new embedding applies to a problem of minimizing the sum of two proper lower-semicontinuous convex functions and can be represented as a single inequality that uses perspectives of these functions and of their conjugates. A solution to the proposed embedding encodes a primal-dual solution to the original problem when available, or an infeasibility certificate otherwise. We then use the Douglas-Rachford algorithm to find a solution to the embedding and discuss its efficient implementation by exploiting the problem structure. The resulting algorithm recovers an existing method for solving quadratic cone programs as a special case. We demonstrate the generality and effectiveness of the algorithm on a class of convex optimization problems with non-smooth objective function and non-conic constraints.
A primal--dual interior-point method for nonsymmetric conic optimization based on a conjugate-free scaling matrix obtained from a single-secant BFGS update of the primal barrier Hessian, which attains an iteration bound of $\mathcal{O}(\sqrt{\nu}\log(1/\varepsilon)$ and is competitive with QICS, a specialized solver fo...
It is shown that strict complementarity, together with a quadratic facial-violation property of the associated complementary faces, implies uniform quadratic growth of both the primal and dual augmented Lagrangians near a strictly complementary solution, and the local equivalence of three regularity conditions is prove...
We develop a unifying variational framework for accelerated primal-dual flows in affinely constrained convex optimization. In particular, it is shown that applying the Euler-Lagrange-Rayleigh (ELR) principle to coupled augmented Bregman Lagrangians systematically generates accelerated dynamics over general Bregman geom...
Using advanced concepts and techniques from convex and variational analysis (such as the notion of twice epi-differentiability), we establish, under a set of appropriate conditions (including a polyhedricity assumption), that the solution to a parameterized nonlinear variational inequality associated with a positively...
Samir Adly, L. Bourdin· Journal of Convex Analysis· 0 citations
The proposed reformulation of the total scaled-gradient variation regularizer achieves PSNR and SSIM competitive with or superior to representative variational methods, especially at high noise levels, and improves the structural reconstruction under dense and sparse scanning.
Hai-Bin Su, Chunlin Wu, Huibin Chang et al.· 0 citations
This work revisits a variant of the Polyak step-size based on Bregman projections due to Kiwiel (1997), and shows that mirror Polyak enjoys guarantees similar to its Euclidean counterpart, automatically adapting to relative notions of smoothness, Lipschitz continuity, or strong convexity.
Frederik Kunstner, Ryan D'Orazio, V. S. Portella et al.· 1 citation
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