Using a high-sensitivity differential scanning microcalorimeter capable of performing cooling scans, we have examined the phase behavior of small unilamellar vesicles (SUV) as a function of time of storage above their order-disorder phase transition. Vesicles composed of dipalmitoylphosphatidylcholine (DPPC) and dimyristoylphosphatidylcholine (DMPC) were examined. Cooling scans on fresh (5-7-h postsonication) samples revealed broad, relatively simple heat capacity peaks (peak temperatures: 19.9 degrees C for DMPC, 37.8 degrees C for DPPC) free of high-temperature spikes or shoulders. Subsequent heating scans displayed a sharp peak characteristic of previously described fusion products formed below the phase transition. SUV samples stored for 1 or more days above their phase transition displayed a moderately broad, high-temperature shoulder (23.8 degrees C for DMPC and 40.2 degrees C for DPPC) in the cooling profile. For DMPC, the enthalpy associated with this peak increased in a first-order fashion with time. Hydrolysis products were not detected until 12-20 days of storage. Both the rate and extent of shoulder appearance increased with temperature (k = 0.0017 h-1, fraction of total enthalpy = 0.1 at 36 degrees C; k = 0.0037 h-1, fraction = 0.2 at 42 degrees C). Freeze-fracture electron micrographs confirmed that an intermediate-sized vesicle population (diameters 400-500 A) appeared in SUV samples stored above their phase transition. Also, the trapped volume of DMPC SUV increased from 0.26 microL/mumol after 17 h of storage to 0.54 microL/mumol after storage for 16 days at 36 degrees C.(ABSTRACT TRUNCATED AT 250 WORDS)
Robust optimization searches for recommendations that are relatively immune to anticipated uncertainty in the problem parameters. Stochasticities are addressed via a set of discrete scenarios. This paper presents applications in which the traditional stochastic linear program fails to identify a robust solution---despite the presence of a cheap robust point. Limitations of piecewise linearization are discussed. We argue that a concave utility function should be incorporated in a model whenever the decision maker is risk averse. Examples are taken from telecommunications and financial planning.robust optimization, telecommunication network, financial planning, nonlinear objective, utility function, decomposition algorithm
We present a unified framework for solving linear and convex quadratic programs via interior point methods. At each iteration, this method solves an indefinite system whose matrix is [_~-2 A v] instead of reducing to obtain the usual AD2A v system. This methodology affords two advantages: (1) it avoids the fill created by explicitly forming the product AD2A v when A has dense columns; and (2) it can easily be used to solve nonseparable quadratic programs since it requires only that D be symmetric. We also present a procedure for converting nonseparable quadratic programs to separable ones which yields computational savings when the matrix of quadratic coefficients is dense.
Abstract. We propose an interior point method for large-scale convex quadratic programming where no assumptions are made about the sparsity structure of the quadratic coefficient matrix Q. The interior point method we describe is a doubly iterative algorithm that invokes a conjugate projected gradient procedure to obtain the search direction. The effect is that Q appears in a conjugate direction routine rather than in a matrix factorization. By doing this, the matrices to be factored have the same nonzero structure as those in linear programming. Further, one variant of this method is theoretically convergent with only one matrix factorization throughout the procedure.
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