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To develop accurate inference algorithms on embedded manifolds such as the Grassmannian, we often employ several optimization tools and incorporate the characteristics of known manifolds as additional constraints. However, a direct analysis of the nature of functions on manifolds is rarely performed. In this paper, we propose an alternative approach to this inference by adopting a statistical pipeline...
The backpressure algorithm has been widely used as a distributed solution to the problem of joint rate control and routing in multi-hop data networks. By controlling a parameter V in the algorithm, the backpressure algorithm can achieve an arbitrarily small utility optimality gap. However, this in turn brings in a large queue length at each node and hence causes large network delay. This phenomenon...
One of the most central problems in viral marketing is Influence Maximization (IM), which finds a set of k seed users who can influence the maximum number of users in online social networks. Unfortunately, all existing algorithms to IM, including the state of the art SSA and IMM, have an approximation ratio of (1 − 1/e − ε). Recently, a generalization of IM, Cost-aware Target Viral Marketing (CTVM),...
Nowadays, there is a fast-paced shift from legacy telecommunication systems to novel Software Defined Network (SDN) architectures that can support on-the-fly network reconfiguration, therefore, empowering advanced traffic engineering mechanisms. Despite this momentum, migration to SDN cannot be realized at once especially in high-end cost networks of Internet Service Providers (ISPs). It is expected...
As software systems become more complex, the number of test cases required for effective testing becomes intractable. Cache misses have been identified as a major factor that affects software execution time. In our current work we target the instruction locality problem in the context of testing.
High precision location information formulates the basis of many modern wireless location based services (LBS). However, location privacy becomes an important issue along with the advantages that LBS offers. In this paper, we first show that, a novel eavesdropper is able to perform position estimation of the agent by purely overhearing the measurement signals between agents and anchors, using a time...
Dynamic programming is a popular optimization technique, developed in the 60's and still widely used today in several fields for its ability to find global optimum. Dynamic Programming Algorithms (DPAs) can be developed in many dimension. However, it is known that if the DPA dimension is greater or equal to two, the algorithm is an NP complete problem. In this paper we present an approximation of...
In this article, a method has been established for optimizing multivariate nonlinear discontinuous cost functions having multiple simple kinks in their domains of definition, by applying simple non-parametric inverse trigonometric functions and combinations thereof as smoothing agents. The original function is locally replaced by these smoothing agents at the points of jump discontinuity, while retaining...
In this paper, an analysis and a discussion about the possibility of fast approximation on stable pricing and allocation of resources in a combinatorial auction are presented. On the discussion, an approximate auction which has VCG-like pricing mechanism is used considering the situation when a cancellation of winner bid(s) occurred after its winner determination. An analysis about stable approximate...
Four algorithms for finding exact Sum Of Squares decompositions of univariate polynomials are proposed. The first algorithm allows to compute rational SOS decompositions, whereas the second and the third allow to compute SOS decompositions in appropriate finite algebraic extensions of the field of rational numbers. A fourth algorithm allows, when possible, to find an SOS decomposition having just...
In this paper, we propose a novel iterative algorithm based on convex approximation for a large class of possibly nonconvex optimization problems. The stationary points of the original problem are found by solving a sequence of successively refined approximate problems. To achieve convergence, the approximate problem only needs to be pseudo-convex while the stepsizes are determined by the exact or...
As the number of samples and dimensionality of optimization problems related to statistics and machine learning explode, block coordinate descent algorithms have gained popularity since they reduce the original problem to several smaller ones. Coordinates to be optimized are usually selected randomly according to a given probability distribution. We introduce an importance sampling strategy that helps...
We study networked unconstrained convex optimization problems where the objective function changes continuously in time. We propose a decentralized algorithm (DePCoT) with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and gradient-based correction steps, while sampling the problem data at a constant sampling period h. Under suitable conditions and for...
Hierarchical alternating least squares (HALS) algorithms are efficient computational methods for nonnegative matrix factorization (NMF). Given an initial solution, HALS algorithms update the solution block by block iteratively so that the error decreases monotonically. However, update rules in HALS algorithms are not well-defined. In addition, due to this problem, the convergence of the sequence of...
We study nonconvex distributed optimization in multi-agent networks. We introduce a novel algorithmic framework for the distributed minimization of the sum of a smooth (possibly nonconvex) function-the agents' sum-utility-plus a convex (possibly nonsmooth) regularizer. The proposed method hinges on successive convex approximation (SCA) techniques while leveraging dynamic consensus as a mechanism to...
In this paper, an improved ant colony algorithm is proposed to solve solving multi-objective flexible shop scheduling problem. Limitations of the traditional ant colony algorithm weighting coefficient method will result in a greater impact on the results because the determination of the weighting factor has greater subjective factors. Proposed algorithm adds a set of BPs to save all the Pareto set...
We study the joint resource allocation problem of a mobile heterogeneous network composed of macro cellular users and Device-to-Device (D2D) links. Macro cellular users are always scheduled on uplink shared resources, whereas D2D links may use uplink shared resources, orthogonal resources reserved exclusively for D2D transmissions, or a combination of both types of resources. A sum utility maximization...
Computing a matching in a graph is one of "the hardest simple problems" in discrete mathematics and computer science. It is simple since most variants of matching can be solved in polynomial time, yet hard because the running times are high and the algorithms are complex. It is even more challenging to design parallel algorithms for matching, since many algorithms rely on searching for long...
Artificial bee colony (ABC) algorithm introduced by D. Karaboga was inspired by the behaviors of real honey bee colonies. The routes of the swarm are exploited according to the neighbor information by employed bees and onlookers in the ABC algorithm. The classic artificial bee colony algorithm as a swarm optimization method is sometimes trapped in local optima. In this paper we propose a hybrid algorithm...
Random medium access control (MAC) is extensively used in wireless communication systems as a means to allocate the shared radio resource in a distributed manner. However, some of the ultimate performance limits, including the maximum throughput, are not well understood due to the non-convex nature of the utility optimization problem. In this paper, we introduce the monotonic optimization method to...
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