This paper is about distributed derivative-based algorithms for solving optimization problems with a separable (potentially nonconvex) objective function and coupled affine constraints. A parallelizable method is proposed that combines ideas from the fields of sequential quadratic programming and augmented Lagrangian algorithms. The method negotiates shared dual variables that may be interprete...

We present a parallel multi-hypothesis template tracking algorithm on heterogeneous platforms using a layered dispatch programming model. The contributions of this work are: an architecture-specific optimised solution for vasculature structure enhancement, an approach to segment the vascular lumen network from volumetric CTA images and a layered dispatch programming model to free the developers...

We consider the entanglement marginal problem, which consists of deciding whether a number reduced density matrices are compatible with an overall separable quantum state. To tackle this we propose hierarchies semidefinite programming relaxations set state marginals admitting fully extension. connect completeness each hierarchy to resolution analog classical problem and thus identify relevant e...

We propose a polynomial algorithm for a separable convex optimization problem with linear constraints. We do not make any additional assumptions about the structure of the objective function except for polynomial computability. That is, the objective function can be non-differentiable. The running time of our algorithm is polynomial in the size of the input consisting of an instance of the prob...

We consider the problem of minimizing a convex separable logarithmic function over a region defined by a convex inequality constraint or linear equality constraint, and twosided bounds on the variables (box constraints). Such problems are interesting from both theoretical and practical point of view because they arise in somemathematical programming problems as well as in various practical prob...

Much fundamental research in computer science is driven by two complementary notions of beauty in programming, that arising from the structure of a program and that arising from the efficiency of an algorithm. As Lewin famously said, “there’s nothing so practical as good theory” [Marrow, 1984]. Decades of experience has borne out the remarkable efficacy of theory in improving the practice of pr...

This paper deals with a certain class of optimization methods, based on conservative convex separable approximations (CCSA), for solving inequality-constrained nonlinear programming problems. Each generated iteration point is a feasible solution with lower objective value than the previous one, and it is proved that the sequence of iteration points converges toward the set of Karush–Kuhn–Tucker...

The nonmonotone linear complementarity problem (LCP) is formulated as a bilinear program with separable constraintsand an objective function that minimizesa natural error residual for the LCP. A linear-programming-basedalgorithm applied to the bilinear program terminates in a nite number of steps at a solution or stationary point of the problem. The bilinear algorithm solved 80 consecutive case...

In this paper we provide a complete analytic solution to a particular separable convex quadratic programming problem with bound and equality constraints. This study constitutes the generalization of prior papers in which additional simplifications were considered. We present an algorithm that leads to determination of the analytic optimal solution. We demonstrate that our algorithm is able to d...