XPS: EXPL: Scalable distributed GPU computing for extremely high
... the dimensionality to up to 100 million variables. The problem decomposition into multiple devices allows for the efficient use of the memory and computational resources, while avoiding synchronization delays or heavy data transfer overheads. It involves heterogeneous compute units to combine both t ...
... the dimensionality to up to 100 million variables. The problem decomposition into multiple devices allows for the efficient use of the memory and computational resources, while avoiding synchronization delays or heavy data transfer overheads. It involves heterogeneous compute units to combine both t ...
Reliable Space Pursuing for Reliability-based Design Optimization with Black-box Performance Functions
... reliability constraints to approximately- equivalent deterministic constraints, based on which a safety-factor based approach was developed [9]. YANG, et al[10], implemented and tested several approximate RBDO methods against a double loop algorithm with a number of design problems. SHAN, et al[11], ...
... reliability constraints to approximately- equivalent deterministic constraints, based on which a safety-factor based approach was developed [9]. YANG, et al[10], implemented and tested several approximate RBDO methods against a double loop algorithm with a number of design problems. SHAN, et al[11], ...
Conservation decision-making in large state spaces
... require the value function to be updated for the entire state space for every time step. The high computational requirements of large SDP problems means that only simple population management problems can be analysed. In this paper we present an application of the on-line sparse sampling algorithm p ...
... require the value function to be updated for the entire state space for every time step. The high computational requirements of large SDP problems means that only simple population management problems can be analysed. In this paper we present an application of the on-line sparse sampling algorithm p ...
Constrained Optimization Methods in Health Services Research
... across the boundaries of mathematics, computer science, economics, and engineering. Analytical foundations for the techniques to solve the constrained optimization problems involving continuous, differentiable functions and equality constraints were already laid in the 18th century [6]. However, wit ...
... across the boundaries of mathematics, computer science, economics, and engineering. Analytical foundations for the techniques to solve the constrained optimization problems involving continuous, differentiable functions and equality constraints were already laid in the 18th century [6]. However, wit ...
Sample Average Approximation of Expected Value Constrained
... has a finite support. He proposed solution techniques including reformulating the problem as one with dual angular structure and using Benders decomposition. Kuhn [7] and Atlason, Epelman and Henderson [2] considered the case where the support of ω is infinite. In [7], the author proposed bounding a ...
... has a finite support. He proposed solution techniques including reformulating the problem as one with dual angular structure and using Benders decomposition. Kuhn [7] and Atlason, Epelman and Henderson [2] considered the case where the support of ω is infinite. In [7], the author proposed bounding a ...
Chapter 8 Notes
... DP solution to the coin-row problem Let F(n) be the maximum amount that can be picked up from the row of n coins. To derive a recurrence for F(n), we partition all the allowed coin selections into two groups: those without last coin – the max amount is ? those with the last coin -- the max amount i ...
... DP solution to the coin-row problem Let F(n) be the maximum amount that can be picked up from the row of n coins. To derive a recurrence for F(n), we partition all the allowed coin selections into two groups: those without last coin – the max amount is ? those with the last coin -- the max amount i ...
