Intel machines

The MINLP-model instances comprised 200 binary variables, 588 continuous variables and 1038 constraints. The linearization not only eliminates the nonlinearity but also leads to a reduced number of398 continuous variables and 830 constraints (the number of 200 binary variables is unchanged). The MINLP-problems were solved by the solver architecture DICOPT/CONOPT/CPLEX, and the MILP problems were solved by CP LEX, both on a Windows machine with an Intel Xeon 3 GHz CPU and 4 GB RAM. 

Laguna, M. J. W. Barnes and F. Glover. Tabu Search Methods for a Single Machine Scheduling Problem. J Intell Manufact 2 63 (1991). 

Thissen, U., Pepers, M., Ustiin, B., Meissen, W. J., Buydens, L. C. M. Chemom. Intell. Lab. Syst. 73, 2004, 169-179. Comparing support vector machines to PLS for spectral regression applications. 

Linnainmaa, S., Harwood, D., and Davis, L.S. Pose determination of a three-dimensional object using triangle pairs. IEEE Trans. PattemAnal. Machine Intell. 1988, 10, 634-646. 

S.J. Dixon and R.G. Brereton, Comparison of performance of five common classifiers represented as boundary methods Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure, Chemom. Intell. Lab. Syst, 95, 1-17 (2009). 

Two numerical examples adapted from [4,10] are supplied to demonstrate the efficacy of proposed HEN synthesis strategy. To solve this MO-MILP for the HEN model, GAMS [16] and CPLEX are used as the modeling environment and the MIP solver, respectively. The computing machine is a personal computer with an Intel Pentium IV 2.26 GHz CPU. 

One way to increase precision, at the expense of computing speed, is to "chain" two or more words to provide a "double-precision" or "extended precision" word. The number of bits reserved to the exponent is sometimes increased, sometimes not, but the mantissa receives lots of extra bits from the extra chained words. This is done routinely for scientific work on 8-bit PDP-8 machines and on the early 16-bit PC s (IBM PC, PC AT, from Intel 80086 to 80186 to 80286 to 80486 CPUs), until 32-bit PC s became the norm (Pentium CPUs). 

The Intel iPSC-860 is a hypercube MIMD system that scales to 128 i860 RISC processing nodes with up to 32 Mbyte of memory. The i860 peak performance is 40 MFLOPS for double-precision matrix multiply and 60 MFLOPS for algorithms that can do two adds and a multiply on the same instruction cycle. The interprocessor communication rate is 2.8 Mbyte/s. Each node runs the proprietary N/X operating system and is limited to a single user process. The machine is space-shared, and each partition must be a subcube of the system that is, the number of nodes used for a particular job must be a power... 

Alliant AT T Convex Cray Digital Evans Sutherland Fujitsu Hewlett-Packard Hitachi IBM Intel Kendall Square Kubota NEC Silicon Graphics Star Sun Thinking Machines, and others. 

When the Pentium II came out, consumers loved it. The only problem was that it was a bit too expensive for use in low-buck machines. To meet that market need, Intel came out with the Celeron processor. It had the computing power roughly equivalent to the Pentium II but cost less. 

A. The IBM PC-AT introduced in 1984 did not feature 128KB RAM. The IBM PC-AT was the first machine shipped with DOS 3. It had 256KB of RAM, an Intel 80286 processor (6 MHz ), and a 1.2MB, 5.25" floppy drive. A 20MB hard drive and color video card were also available. 

Various companies associated with the high-tech revolution have operated or continue to operate businesses in Silicon Valley. For example. International Business Machines (IBM) built its Pacific headquarters in San Jose in 1943. Since that time, the Silicon Valley has been home to corporations such as Apple, eBay, Google, Hewlett-Packard, Intel, Lockheed, San Disk, Sun Microsystems, Yahool, and many others. 

C. de Boor A Practical Guide to Splines. Springer 1978 J.Lane and R.Riesenfeld A theoretical development for the computer generation and display of piecewise polynomial surfaces. IEEE Trans Pattern Anal. Machine Intel 2(1), pp35-46, 1980... 

H. Kwon and N. M. Nasrabadi, Kernel matched subspace detectors for hyperspeetral target detection. IEEE Trans. Pattern Anal. Machine Intell. 28(2) 178-194, (2006). 

Li, J., Liu, H Yao, X.-J., Liu, M., Hu, Z. and Fan, B.T. (2007) Quantitative structure-activity relationship study of acyl ureas as inhibitors of human liver glycogen phosphorylase using least squares support vector machines. Chemom. Intell. Lab. Syst., 87, 139-146. 

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