Optimization

By William M. Waite. 2005

2,568 citations recorded by the source corpus.

Optimization is generally a process of learning and unlearning, and of trials and errors, precisely like a mindsponge process.

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Citations in the corpus

Best Subset Selection via a Modern Optimization Lens (2015, methodology): “Some examples of modern integer optimization solvers include CPLEX, GLPK, MOSEK and GUROBI. In the period 1991–2015, the computational power of MIO solvers has increased at an astonishing rate. In Bixby (2012), to measure the speedup of MIO solvers, the same set of MIO problems were tested on the sa”
Taskonomy: Disentangling Task Transfer Learning (2018, methodology): “We solve this subgraph selection problem us-ing Boolean Integer Programming (BIP), described below, which can be solved optimally and efﬁciently [41, 16].”
Learning Where to Attend with Deep Architectures for Image Tracking (2011, methodology): “In this paper we use GP-UCB (Srinivas et al., 2010) which selects where β t is a parameter.”
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics (2016, methodology): “We evaluated a text classification pipeline based on [39] on the Amazon Reviews dataset of 65m product reviews [42] with 100k sparse features.”
Rectified Factor Networks (2015, methodology): “(3), we use the generalized reduced method [17].”
Ant colony system: a cooperative learning approach to the traveling salesman problem (1997, unknown): “Ant system [10] is the progenitor of all our research efforts with ant algorithms, and was first applied to the traveling salesman problem (TSP), which is defined in Fig.”
MAX-MIN Ant System (2000, unknown): “Ant Colony Optimization (ACO) [13,8,14,11] is a recently de veloped, populationbased approach which has been successfully applied to sever alNP-hard combinatorial optimization problems [5,7,12,19,20,29,35,45] (s ee [10,11] for an overview).”
A social spider algorithm for global optimization (2015, methodology): “Meanwhile, ACO uses the path between the ant hive and food sources to represent solutions to the optimization problems.”
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization (2002, unknown): “For an overview of SQP methods, see, for example, Boggs and Tolle [6], Fletcher [31], Gill, Murray, and Wright [48], Murray [56], and Powell [66].”
Ant Colony Optimization: Overview and Recent Advances (2018, methodology): “These three algorithms were proposed in Dorigo’s doctoral dissertation [55] and first appeared in a technical report [63, 64] that was published a few years later in the IEEE Transactions on Systems, Man, and Cybernetics [65].”
Enhancing Sparsity by Reweighted ℓ1 Minimization (2007, background): “The iterative reweighted algorithm falls in the general class of MM algorithms, see [36] and references therein.”
Bio-inspired computation: Where we stand and what's next (2019, background): “This is essentially the fundamental principle behind Ant Colony Optimization (ACO, [23]) and Particle Swarm Optimization (PSO, [24]), arguably the”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “[197] J. Handl, J. Knowles, M. Dorigo, Ant-Based Clustering: a Comparative Study of its Relative Performance with Respect to k-means, Average Link and 1DSOM, Technical Report TR/IRIDIA/2003-24, IRIDIA, University Libre de Bruxelles, Belgium, 2003.”
Grothendieck's Theorem, past and present (2011, background): “The analogue of this fact (with ρ = 16/17) for MAX CUT goes back to H̊astad [61] (see also [111]).”
D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization (2012, background): “We assume the reader is familiar with these concepts, as well as with Lagrangian duality [1], [5].”
Optimal design with probabilistic objective and constraints (2006, background): “2 in Royset et al. (2002), which is originally due to Gonzaga and Polak (1979),”