Abstract In this paper, we present a limited-memory common-directions method for smooth optimization that interpolates between first- and second-order methods. At each iteration, subspace of limited dimension size is constructed using first-order information from previous iterations, an efficient Newton deployed to find approximate minimizer within subspace. With properly selected as small two,...

Massive data analysis in cloud-scale data centers plays a crucial role in making critical business decisions. Highlevel scripting languages free developers from understanding various system trade-offs, but introduce new challenges for query optimization. One key optimization challenge is missing accurate data statistics, typically due to massive data volumes and their distributed nature, comple...

At the global as well as local scales, some of the geometry of types of neuron arbors-both dendrites and axons-appears to be self-organizing: Their morphogenesis behaves like flowing water, that is, fluid dynamically; waterflow in branching networks in turn acts like a tree composed of cords under tension, that is, vector mechanically. Branch diameters and angles and junction sites conform sign...

Large-scale global optimization (LSGO) algorithms are crucially important to handle real-world problems. Recently, cooperative co-evolution (CC) algorithms have successfully been applied for solving many large-scale practical problems. Many applications have imbalanced subcomponents where the size of subcomponents and their contribution to the objective function value are different. CC algorith...

We design algorithms for online linear optimization that have optimal regret and at the same time do not need to know any upper or lower bounds on the norm of the loss vectors. We achieve adaptiveness to norms of loss vectors by scale invariance, i.e., our algorithms make exactly the same decisions if the sequence of loss vectors is multiplied by any positive constant. Our algorithms work for a...

We investigate the fundamental principles that drive the development of scalable algorithms for network optimization. Despite the significant amount of work on parallel and decentralized algorithms in the optimization community, the methods that have been proposed typically rely on strict separability assumptions for objective function and constraints. Beside sparsity, these methods typically d...

Circuit tuning is an important task in the design of custom digital integrated circuits such as high-performance microprocessors. The goal is to improve certain aspects of the circuit, such as speed, area, or power, by optimally choosing the sizes of the transistors. This task can be formulated as a large-scale nonlinear, nonconvex optimization problem, where function values and derivatives are...

6 Simulated Moving Bed (SMB) processes are widely used in sugar, petrochemical, 7 and pharmaceutical industries. However, systematic optimization of SMB, especially 8 finding the optimal zone configuration including the standard and modified nonstan9 dard configurations, is still a challenging problem. This paper proposes a simultane10 ous, fully discretized approach with an SMB superstructure ...

Complex networks are mapped to a model of boxes and balls where the balls are distinguishable. It is shown that the scale-free size distribution of boxes maximizes the information associated with the boxes provided configurations including boxes containing a finite fraction of the total amount of balls are excluded. It is conjectured that for a connected network with only links between differen...

Marketing organizations often design multiple marketing campaigns to meet a variety of objectives. Marketing resources must be allocated carefully across campaigns to minimize conflicts and maximize organizational value (e.g., impact on customer lifetime value). While data-driven analytics has greatly improved the ability to predict the effect of marketing actions on individual customers, such ...