509,753 research outputs found
The Online Knapsack Problem with Departures
The online knapsack problem is a classic online resource allocation problem
in networking and operations research. Its basic version studies how to pack
online arriving items of different sizes and values into a capacity-limited
knapsack. In this paper, we study a general version that includes item
departures, while also considering multiple knapsacks and multi-dimensional
item sizes. We design a threshold-based online algorithm and prove that the
algorithm can achieve order-optimal competitive ratios. Beyond worst-case
performance guarantees, we also aim to achieve near-optimal average performance
under typical instances. Towards this goal, we propose a data-driven online
algorithm that learns within a policy-class that guarantees a worst-case
performance bound. In trace-driven experiments, we show that our data-driven
algorithm outperforms other benchmark algorithms in an application of online
knapsack to job scheduling for cloud computing
Linear Capacity Scaling in Wireless Networks: Beyond Physical Limits?
We investigate the role of cooperation in wireless networks subject to a
spatial degrees of freedom limitation. To address the worst case scenario, we
consider a free-space line-of-sight type environment with no scattering and no
fading. We identify three qualitatively different operating regimes that are
determined by how the area of the network A, normalized with respect to the
wavelength lambda, compares to the number of users n. In networks with
sqrt{A}/lambda < sqrt{n}, the limitation in spatial degrees of freedom does not
allow to achieve a capacity scaling better than sqrt{n} and this performance
can be readily achieved by multi-hopping. This result has been recently shown
by Franceschetti et al. However, for networks with sqrt{A}/lambda > sqrt{n},
the number of available degrees of freedom is min(n, sqrt{A}/lambda), larger
that what can be achieved by multi-hopping. We show that the optimal capacity
scaling in this regime is achieved by hierarchical cooperation. In particular,
in networks with sqrt{A}/lambda> n, hierarchical cooperation can achieve linear
scaling.Comment: 10 pages, 5 figures, in Proc. of IEEE Information Theory and
Applications Workshop, Feb. 201
An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories
We introduce a new class of data-driven and distribution-free
optimistic-robust bimodal inventory optimization (BIO) strategy to effectively
allocate inventory across a retail chain to meet time-varying, uncertain
omnichannel demand. While prior Robust optimization (RO) methods emphasize the
downside, i.e., worst-case adversarial demand, BIO also considers the upside to
remain resilient like RO while also reaping the rewards of improved
average-case performance by overcoming the presence of endogenous outliers.
This bimodal strategy is particularly valuable for balancing the tradeoff
between lost sales at the store and the costs of cross-channel e-commerce
fulfillment, which is at the core of our inventory optimization model. These
factors are asymmetric due to the heterogenous behavior of the channels, with a
bias towards the former in terms of lost-sales cost and a dependence on network
effects for the latter. We provide structural insights about the BIO solution
and how it can be tuned to achieve a preferred tradeoff between robustness and
the average-case. Our experiments show that significant benefits can be
achieved by rethinking traditional approaches to inventory management, which
are siloed by channel and location. Using a real-world dataset from a large
American omnichannel retail chain, a business value assessment during a peak
period indicates over a 15% profitability gain for BIO over RO and other
baselines while also preserving the (practical) worst case performance
Minimizing internal speedup for performance guaranteed optical packet switches
Providing QoS guarantee for Internet services is very important It evokes the issue that packet switches should provide guaranteed performance (i.e. 100% throughput with bounded worst-case delay). Optical switching technology is widely considered as an excellent solution for packet switches in future networks. However, to achieve guaranteed performance in optical packet switches, an internal speedup is required due to the existence of reconfiguration overhead. How to reduce the internal speedup is the main concern for making these switches practical In this paper, we first derive the internal speedup S as a function of the number of switch configurations N S and the reconfiguration overhead δ, or S=f(N S,δ). We show that the recently proposed ADJUST algorithm is flawed. Based on the internal speedup function we derived, a new algorithm (ADAPTIVE), with time complexity of O((λ-l)N 2logN), is proposed to minimize S. © 2004 IEEE.published_or_final_versio
Subset-Based Instance Optimality in Private Estimation
We propose a new definition of instance optimality for differentially private
estimation algorithms. Our definition requires an optimal algorithm to compete,
simultaneously for every dataset , with the best private benchmark algorithm
that (a) knows in advance and (b) is evaluated by its worst-case
performance on large subsets of . That is, the benchmark algorithm need not
perform well when potentially extreme points are added to ; it only has to
handle the removal of a small number of real data points that already exist.
This makes our benchmark significantly stronger than those proposed in prior
work. We nevertheless show, for real-valued datasets, how to construct private
algorithms that achieve our notion of instance optimality when estimating a
broad class of dataset properties, including means, quantiles, and
-norm minimizers. For means in particular, we provide a detailed
analysis and show that our algorithm simultaneously matches or exceeds the
asymptotic performance of existing algorithms under a range of distributional
assumptions
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