UNIPOL: Unimodular sequence design via a separable iterative quartic polynomial optimization for active sensing systems

Polynomial Methods for Separable Convex Optimization in Unimodular Linear Spaces with Applications

We consider the problem of minimizing a separable convex objective function over the linear space given by system Mx = 0 with M a totally unimodular matrix. In particular, this generalizes the usual minimum linear cost circulation and co-circulation problems in a network, and the problems of determining the Euclidean distance from a point to the perfect bipartite matching polytope and the feasi...

Monomial-wise optimal separable underestimators for mixed-integer polynomial optimization

In this paper we introduce a new method for solving box-constrained mixed-integer polynomial problems to global optimality. The approach, a specialized branch-and-bound algorithm, is based on the computation of lower bounds provided by the minimization of separable underestimators of the polynomial objective function. The underestimators are the novelty of the approach because the standard appr...

Iterative design space exploration and robustness optimization for embedded systems

Presenting Three Design Methods for Axial Compressor Blade via Optimization

Improving the efficiency of compressors has been one of the most important goals of researchers over the years. In this paper, three different methods are presented for parameterization and blade optimization of axial flow compressor. All methods consist of flow analysis tool, optimization algorithms, and parametric geometry generation tool, that are different in each approach. Objective functi...

Separable Approximate Optimization of Support Vector Machines for Distributed Sensing

Sensor measurements from diverse locations connected with possibly low bandwidth communication channels pose a challenge of resource-restricted distributed data analyses. In such settings it would be desirable to perform learning in each location as much as possible, without transferring all data to a central node. Applying the support vector machines (SVMs) with nonlinear kernels becomes nontr...