1,407 research outputs found
Decomposition-Based Method for Sparse Semidefinite Relaxations of Polynomial Optimization Problems
We consider polynomial optimization problems pervaded by a sparsity pattern. It has been shown in [1, 2] that the optimal solution of a polynomial programming problem with structured sparsity can be computed by solving a series of semidefinite relaxations that possess the same kind of sparsity. We aim at solving the former relaxations with a decompositionbased method, which partitions the relaxations according to their sparsity pattern. The decomposition-based method that we propose is an extension to semidefinite programming of the Benders decomposition for linear programs [3] .Polynomial optimization, Semidefinite programming, Sparse SDP relaxations, Benders decomposition
Linear Programming Relaxations of Quadratically Constrained Quadratic Programs
We investigate the use of linear programming tools for solving semidefinite
programming relaxations of quadratically constrained quadratic problems.
Classes of valid linear inequalities are presented, including sparse PSD cuts,
and principal minors PSD cuts. Computational results based on instances from
the literature are presented.Comment: Published in IMA Volumes in Mathematics and its Applications, 2012,
Volume 15
On Semidefinite Programming Relaxations of the Travelling Salesman Problem (Replaced by DP 2008-96)
AMS classification: 90C22, 20Cxx, 70-08traveling salesman problem;semidefinite programming;quadratic as- signment problem
A Polynomial Optimization Approach to Constant Rebalanced Portfolio Selection
We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on semidefinite programming. Our algorithm can solve problems that can not be handled by any of known polynomial optimization solvers.Multi-period portfolio optimization;Polynomial optimization problem;Constant rebalancing;Semidefinite programming;Mean-variance criterion
Equivalent relaxations of optimal power flow
Several convex relaxations of the optimal power flow (OPF) problem have
recently been developed using both bus injection models and branch flow models.
In this paper, we prove relations among three convex relaxations: a
semidefinite relaxation that computes a full matrix, a chordal relaxation based
on a chordal extension of the network graph, and a second-order cone relaxation
that computes the smallest partial matrix. We prove a bijection between the
feasible sets of the OPF in the bus injection model and the branch flow model,
establishing the equivalence of these two models and their second-order cone
relaxations. Our results imply that, for radial networks, all these relaxations
are equivalent and one should always solve the second-order cone relaxation.
For mesh networks, the semidefinite relaxation is tighter than the second-order
cone relaxation but requires a heavier computational effort, and the chordal
relaxation strikes a good balance. Simulations are used to illustrate these
results.Comment: 12 pages, 7 figure
Online Local Learning via Semidefinite Programming
In many online learning problems we are interested in predicting local
information about some universe of items. For example, we may want to know
whether two items are in the same cluster rather than computing an assignment
of items to clusters; we may want to know which of two teams will win a game
rather than computing a ranking of teams. Although finding the optimal
clustering or ranking is typically intractable, it may be possible to predict
the relationships between items as well as if you could solve the global
optimization problem exactly.
Formally, we consider an online learning problem in which a learner
repeatedly guesses a pair of labels (l(x), l(y)) and receives an adversarial
payoff depending on those labels. The learner's goal is to receive a payoff
nearly as good as the best fixed labeling of the items. We show that a simple
algorithm based on semidefinite programming can obtain asymptotically optimal
regret in the case where the number of possible labels is O(1), resolving an
open problem posed by Hazan, Kale, and Shalev-Schwartz. Our main technical
contribution is a novel use and analysis of the log determinant regularizer,
exploiting the observation that log det(A + I) upper bounds the entropy of any
distribution with covariance matrix A.Comment: 10 page
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