Online knapsack problem is considered, where items arrive in a sequential fashion that have two attributes; value and weight. Each arriving item has to be accepted or rejected on its arrival irrevocably. The objective is to maximize the sum of the value of the accepted items such that the sum of their weights is below a budget/capacity. Conventionally a hard budget/capacity constraint is consid...

Over the last decade, proliferation of various online platforms and their increasing adoption by billions of users have heightened the privacy risk of a user enormously. In fact, security researchers have shown that sparse microdata containing information about online activities of a user although anonymous, can still be used to disclose the identity of the user by cross-referencing the data wi...

We present a novel constraint-partitioning approach for solving continuous nonlinear optimization based on augmented Lagrange method. In contrast to previous work, our approach is based on a new constraint partitioning theory and can handle global constraints. We employ a hyper-graph partitioning method to recognize the problem structure. We prove global convergence under assumptions that are m...

We study the growth of random networks under a constraint that the diameter, defined as the average shortest path length between all nodes, remains approximately constant. We show that if the graph maintains the form of its degree distribution then that distribution must be approximately scale-free with an exponent between 2 and 3. The diameter constraint can be interpreted as an environmental ...

Nonclassical states of light play a central role in many quantum information protocols. Very recently, their quantum features have been exploited to improve the readout of information from digital memories, modeled as arrays of microscopic beam splitters [Pirandola, Phys. Rev. Lett. 106, 090504 (2011)]. In this model of “quantum reading,” a nonclassical source of light with Einstein-Podolski-Ro...

A new method is proposed in this paper to learn overcomplete dictionary from training data samples. Differing from the current methods that enforce similar sparsity constraint on each of the input samples, the proposed method attempts to impose global sparsity constraint on the entire data set. This enables the proposed method to fittingly assign the atoms of the dictionary to represent various...

The Distributed Constraint Optimization Problem (DCOP) is able to model many problems in multiagent systems but existing research has not considered the issue of unreliable communication which often arises in real world applications. Limited bandwidth, interference, loss of line-of-sight are some reasons why communication fails in the real world. In this paper we show that an existing asynchron...

The three Instruction Set Extension (ISE) enumeration algorithms described in this paper are Subgraph Enumeration (SE), Subgraph Removal (SR), and Lucky Subgraph Removal (LSR). SE exhaustively enumerates all convex subgraphs of a dataflow graph. SR iteratively finds the highest gain subgraph and then locks the related nodes out of the solution space for the next iteration of the search. Finally...

The recently formulated metabolic theory of ecology has profound implications for the evolution of life histories. Metabolic rate constrains the scaling of production with body mass, so that larger organisms have lower rates of production on a mass-specific basis than smaller ones. Here, we explore the implications of this constraint for life-history evolution. We show that for a range of very ...

Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i), its probability of type I error is below a pre-specifi...