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Identifying small groups of lines, whose removal would cause a severe blackout, is critical for the secure operation of the electric power grid. We show how power grid vulnerability analysis can be studied as a bilevel mixed integer nonlinear programming problem. Our analysis reveals a special structure in the formulation that can be exploited to avoid(More)
The self-consistent field (SCF) iteration, widely used for computing the ground state energy and the corresponding single particle wave functions associated with a many-electron atomistic system, is viewed in this paper as an optimization procedure that minimizes the Kohn– Sham (KS) total energy indirectly by minimizing a sequence of quadratic surrogate(More)
We propose a new process using a vapor phase bioreactor (VPB) to simultaneously (i) delignify sugar-cane bagasse, a residue of sugar production that can be recycled in paper industry, and (ii) produce laccase, an enzyme usable to bleach paper pulp. Ethanol vapor, used as laccase inducer, was blown up through a VPB packed with bagasse and inoculated with(More)
We describe an efficient implementation of an algorithm for computing selected elements of a general sparse symmetric matrix <i>A</i> that can be decomposed as <i>A</i> = <i>LDLT</i>, where <i>L</i> is lower triangular and <i>D</i> is diagonal. Our implementation, which is called <i>SelInv</i>, is built on top of an efficient supernodal left-looking(More)
We describe the design and implementation of KSSOLV, a MATLAB toolbox for solving a class of nonlinear eigenvalue problems known as the <i>Kohn-Sham equations</i>. These types of problems arise in electronic structure calculations, which are nowadays essential for studying the microscopic quantum mechanical properties of molecules, solids, and other(More)
A new direct constrained optimization algorithm for minimizing the Kohn-Sham (KS) total energy functional is presented in this paper. The key ingredients of this algorithm involve projecting the total energy functional into a sequences of sub-spaces of small dimensions and seeking the minimizer of total energy functional within each subspace. The minimizer(More)
—We propose a computationally efficient approach to detect severe multiple contingencies. We pose a contingency analysis problem using a nonlinear optimization framework, which enables us to detect the fewest possible transmission line outages resulting in a system failure of specified severity, and to identify the most severe system failure caused by(More)
Object-oriented programming is a relatively new tool in the development of optimization software. The code extensibility and the rapid algorithm prototyping capability enabled by this programming paradigm promise to enhance the reliability, utility, and ease of use of optimization software. While the use of object-oriented programming is growing, there are(More)
We investigate the convergence of the self-consistent field (SCF) iteration used to solve a class of nonlinear eigenvalue problems. We show that for the class of problem considered, the SCF iteration produces a sequence of approximate solutions that contain two convergent subsequences. These subsequences may converge to two different limit points, neither(More)
A m ultigrid preconditioned conjugate gradient algorithm is introduced into a semiconductor device modeling code, DANCIR. This code simulates a wide variety of semiconductor devices by n umericallysolving the drift-diiusion equations. The most time consuming aspect of the simulation is the solution of three linear systems within each iteration of the Gummel(More)