Maximization (psychology)

Maximization is a style of decision-making characterized by seeking the best option through an exhaustive search through alternatives. It is contrasted with satisficing, in which individuals evaluate options until they find one that is "good enough". Definition The distinction between "maximizing" and "satisficing" was first made by Herbert A. Simon in 1956. Simon noted that although fields like economics posited maximization or "optimizing" as the rational method of making decisions, humans often lack the cognitive resources or the environmental affordances to maximize. Simon instead formulated an approach known as bounded rationality, which he also referred to as satisficing. This approach was taken to be adaptive and, indeed, necessary, given our cognitive limitations. Thus, satisficing was taken to be a universal of human cognition. Although Simon's work on bounded rationality was influential and can be seen as the origin of behavioral economics, the distinction betwee

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State-of-the-art image and action classification systems often employ vocabulary-based representations. The classification accuracy achieved with such vocabulary-based representations depends significantly on the chosen histogram-distance. In particular, when the decision function is a support-vector-machine (SVM), the classification accuracy depends on the chosen histogram kernel. In this paper we focus on smoothly-parameterized kernels in the space of histograms, such as, but not limited to, kernels that are derived from smoothly-parameterized histogram-distance functions. We learn parameters of histogram kernels so that the SVM accuracy is improved. This is accomplished by simultaneously maximizing the SVM's geometric margin and minimizing an estimate of its generalization error. We validate our approach on a previously-published two-class synthetic dataset and three real-world multi-class datasets: Oxford5K, KTH, and UCF. On these datasets our approach yields results that compare favorably to or exceed the state of the art.

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Cyme: A Library Maximizing SIMD Computation on User-Defined Containers

Timothée Ewart, Felix Schürmann

This paper presents Cyme, a C++ library aiming at abstracting the usage of SIMD instructions while maximizing the usage of the underlying hardware. Unlike similar efforts such as Boost.simd or VC, Cyme provides generic high level containers to the users which hides SIMD complexity. Cyme accomplishes this by 1) optimization of the Abstract Syntax Tree using Expression Templates Programming to prevent temporary copies and maximize the use of Fuse Multiply Add instructions and 2) creating a data layout in memory (AoS or AoSoA), which minimizes data addressing and manipulation throughout all SIMD registers. Implementation of Cyme library has been accomplished on the IBM Blue Gene/Q architecture using the 256 bit SIMD extensions (QPX) of the Power A2 processor. Functionality of the library is demonstrated on a computationally intensive kernel of a neuro-scientific application where an increase of GFlop/s performance by a factor of 6.72 over the original implementation is observed using Clang compiler

Springer International Publishing2014

Satisficing measure approach for vehicle routing problem with time windows under uncertainty

Jingjing Jiang

The complexity of evaluating chance constraints makes chance-constrained programming problem difficult to solve. One way to handle this complexity is by devising satisficing measures for the relevant uncertainties. This paper focuses on solving the stochastic vehicle routing problem with time windows (VRPTW) by Satisficing Measure Approach (SMA) that mitigates the dissatisfaction experienced by the customers. Satisficing measures are first proposed for the VRPTW with stochastic demand on various distributions to demonstrate the dependency of customers' satisfaction towards lack of inventory based on the vehicle's capacity. Similar satisficing measures are extended to VRPTW with stochastic travel times. We integrate the proposed satisficing measures into an existing tabu-search heuristics to solve a set of generalized Solomon instances in a short amount of computation time. Compared with best-known results, the SMA saves the effort to design recourse actions, applicable to many popular probability distributions and produces very competitive results. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

2016

MGT-418: Convex optimization

This course introduces the theory and application of modern convex optimization from an engineering perspective.

Behavioral economics

Behavioral economics studies the effects of psychological, cognitive, emotional, cultural and social factors in the decisions of individuals or institutions, and how these decisions deviate from tho