𝔖 Bobbio Scriptorium
✦   LIBER   ✦

On parametric semidefinite programming

✍ Scribed by D. Goldfarb; K. Scheinberg

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
869 KB
Volume
29
Category
Article
ISSN
0168-9274

No coin nor oath required. For personal study only.

✦ Synopsis

In this paper we consider a semidefinite programming (SDP) problem in which the objective function depends linearly on a scalar parameter. We study the properties of the optimal objective function value as a function of that parameter and extend the concept of the optimal partition and its range in linear programming to SDP. We also consider an approach to sensitivity analysis in SDP and the extension of our results to an SDP problem with a parametric right-hand side.

πŸ“œ SIMILAR VOLUMES

Applications of semidefinite programming
Applications of semidefinite programming
✍ Lieven Vandenberghe; Stephen Boyd πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 871 KB

A wide variety of nonlinear convex optimization problems can be cast as problems involving linear matrix inequalities (LMIs), and hence efficiently solved using recently developed interior-point methods. In this paper, we will consider two classes of optimization problems with LMI constraints: (1)

Semidefinite programming and matrix scal
Semidefinite programming and matrix scaling over the semidefinite cone
✍ Bahman Kalantari πŸ“‚ Article πŸ“… 2003 πŸ› Elsevier Science 🌐 English βš– 269 KB

Let E be the Hilbert space of real symmetric matrices with block diagonal form diag(A, M), where A is n Γ— n, and M is an l Γ— l diagonal matrix, with the inner product x, y ≑ Trace(xy). We assume n + l 1, i.e. allow n = 0 or l = 0. Given x ∈ E, we write x 0 (x 0) if it is positive semidefinite (posit

Subgraph Matching with Semidefinite Prog
Subgraph Matching with Semidefinite Programming
✍ C. Schellewald; C. SchnΓΆrr πŸ“‚ Article πŸ“… 2003 πŸ› Elsevier Science 🌐 English βš– 539 KB

We present a convex programming approach to the problem of matching subgraphs which represent object views against larger graphs which represent scenes. Starting from a linear programming formulation for computing optimal matchings in bipartite graphs, we extend the linear objective function in orde