Iteration complexity of randomized block
... methods in the smooth unconstrained and box-constrained setting, in effect extending and improving upon some of the results in [6], [3], [18] in several ways. While the asymptotic convergence rates of some variants of CD methods are well understood [9], [23], [21], [31], iteration complexity results ...
... methods in the smooth unconstrained and box-constrained setting, in effect extending and improving upon some of the results in [6], [3], [18] in several ways. While the asymptotic convergence rates of some variants of CD methods are well understood [9], [23], [21], [31], iteration complexity results ...
CV - Jeff Clune
... presentation at the Neural Information Processing Systems (NIPS) Feature Extraction Workshop (6.7% oral acceptance rate). Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H (2015) Understanding neural networks through Deep Visualization. International Conference on Machine Learning (ICML) Deep Learnin ...
... presentation at the Neural Information Processing Systems (NIPS) Feature Extraction Workshop (6.7% oral acceptance rate). Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H (2015) Understanding neural networks through Deep Visualization. International Conference on Machine Learning (ICML) Deep Learnin ...
Achieving Maximum Energy-Efficiency in Multi-Relay
... frequency division multiple access (OFDMA) cellular network, where the objective function is formulated as the ratio of the spectral-efficiency (SE) over the total power dissipation. It is proven that the fractional programming problem considered is quasi-concave so that Dinkelbach’s method may be e ...
... frequency division multiple access (OFDMA) cellular network, where the objective function is formulated as the ratio of the spectral-efficiency (SE) over the total power dissipation. It is proven that the fractional programming problem considered is quasi-concave so that Dinkelbach’s method may be e ...
Mining Spatial Trends by a Colony of Cooperative Ant Agents
... gets a specified start object o from the user. Then it has to examine every possible path in the graph beginning from o. For each path it performs a regression analysis on nonspatial values of the path vertices and their distance from o. But the search space soon becomes tremendously huge by increas ...
... gets a specified start object o from the user. Then it has to examine every possible path in the graph beginning from o. For each path it performs a regression analysis on nonspatial values of the path vertices and their distance from o. But the search space soon becomes tremendously huge by increas ...
as a PDF
... However, it is not blindingly fast, so various efforts were made to speed it up, mostly well-known standard techniques in symbolic search such as forward set simplification. A bigger gain in efficiency was achieved by using bidirectional search, which can be incorporated into the algorithm in a stra ...
... However, it is not blindingly fast, so various efforts were made to speed it up, mostly well-known standard techniques in symbolic search such as forward set simplification. A bigger gain in efficiency was achieved by using bidirectional search, which can be incorporated into the algorithm in a stra ...
On the Use of Non-Stationary Strategies for Solving Two
... mostly on the optimal strategy μ∗ . However, estimating at each iteration Tμk−1 ...Tμ0 0 requires solving a control problem and thus might be computationally prohibitive. Therefore, we introduce a second version of PSDP for games, which does not require to solve a control problem at each iteration. ...
... mostly on the optimal strategy μ∗ . However, estimating at each iteration Tμk−1 ...Tμ0 0 requires solving a control problem and thus might be computationally prohibitive. Therefore, we introduce a second version of PSDP for games, which does not require to solve a control problem at each iteration. ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.