Learning Robust Search Strategies Using a Bandit-Based Approach

Effective solving of constraint problems often requires choosing good or specific search heuristics. However, choosing or designing a good search heuristic is non-trivial and is often a manual process. In this paper, rather than manually choosing/designing search heuristics, we propose the use of bandit-based learning techniques to automatically select search heuristics. Our approach is online where the solver learns and selects from a set of heuristics during search. The goal is to obtain automatic search heuristics which give robust performance. Preliminary experiments show that our adaptive technique is more robust than the original search heuristics. It can also outperform the original heuristics.Comment: Published at the Proceedings of 32th AAAI Conference on Artificial Intelligence (AAAI'18

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation

Sparse kernel density estimation technique based on zero-norm constraint

A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity. The classical Parzen window estimate is adopted as the desired response for density estimation, and an approximate function of the zero-norm is used for achieving mathemtical tractability and algorithmic efficiency. Under the mild condition of the positive definite design matrix, the kernel weights of the proposed density estimator based on the zero-norm approximation can be obtained using the multiplicative nonnegative quadratic programming algorithm. Using the -optimality based selection algorithm as the preprocessing to select a small significant subset design matrix, the proposed zero-norm based approach offers an effective means for constructing very sparse kernel density estimates with excellent generalisation performance

Proof of a Conjecture of Hirschhorn and Sellers on Overpartitions

Let $\bar{p}(n)$ denote the number of overpartitions of $n$. It was conjectured by Hirschhorn and Sellers that \bar{p}(40n+35)\equiv 0\ ({\rm mod\} 40) for $n\geq 0$. Employing 2-dissection formulas of quotients of theta functions due to Ramanujan, and Hirschhorn and Sellers, we obtain a generating function for $\bar{p}(40n+35)$ modulo 5. Using the $(p, k)$-parametrization of theta functions given by Alaca, Alaca and Williams, we give a proof of the congruence \bar{p}(40n+35)\equiv 0\ ({\rm mod\} 5). Combining this congruence and the congruence \bar{p}(4n+3)\equiv 0\ ({\rm mod\} 8) obtained by Hirschhorn and Sellers, and Fortin, Jacob and Mathieu, we give a proof of the conjecture of Hirschhorn and Sellers.Comment: 11 page

Low-Voltage High-Linearity Wideband Current Differencing Transconductance Amplifier and Its Application on Current-Mode Active Filter

A low-voltage high-linearity wideband current differencing transconductance amplifier (CDTA) is presented in this paper. The CDTA consists of a current differencing circuit and a cross-coupling transconductance circuit. The PSPICE simulations of the proposed CDTA show a good performance: -3dB frequency bandwith is about 900 MHz, low power consumption is 2.48 mW, input current linear range is ±100 µA and low current-input resistance is less than 20 Ω, high current-output resistance is more than 3 MΩ. PSpice simulations for a current-mode universal filter and a proposed high-order filter are also conducted, and the results verify the validity of the proposed CDTA