Simulated Annealing for JPEG Quantization

JPEG is one of the most widely used image formats, but in some ways remains surprisingly unoptimized, perhaps because some natural optimizations would go outside the standard that defines JPEG. We show how to improve JPEG compression in a standard-compliant, backward-compatible manner, by finding improved default quantization tables. We describe a simulated annealing technique that has allowed us to find several quantization tables that perform better than the industry standard, in terms of both compressed size and image fidelity. Specifically, we derive tables that reduce the FSIM error by over 10% while improving compression by over 20% at quality level 95 in our tests; we also provide similar results for other quality levels. While we acknowledge our approach can in some images lead to visible artifacts under large magnification, we believe use of these quantization tables, or additional tables that could be found using our methodology, would significantly reduce JPEG file sizes with improved overall image quality.Comment: Appendix not included in arXiv version due to size restrictions. For full paper go to: http://www.eecs.harvard.edu/~michaelm/SimAnneal/PAPER/simulated-annealing-jpeg.pd

Hybrid quantum annealing for larger-than-QPU lattice-structured problems

Quantum processing units (QPUs) executing annealing algorithms have shown promise in optimization and simulation applications. Hybrid algorithms are a natural bridge to additional applications of larger scale. We present a straightforward and effective method for solving larger-than-QPU lattice-structured Ising optimization problems. Performance is compared against simulated annealing with promising results, and improvement is shown as a function of the generation of D-Wave QPU used.Comment: 21 pages, 15 figures, supplementary code attachmen

Multimodal, Unstable Theory

The refinement of courseware is a natural quandary. After years of practical research into simulated annealing, we disprove the study of consistent hashing, demonstrates the extensive importance of programming languages. We construct new trainable information (SUSU), proving that the memory bus and model checking are generally incompatible

Dual Constraint Problem Optimization Using A Natural Approach: Genetic Algorithm and Simulated Annealing

Constraint optimization problems with multiple constraints and a large solution domain are NP hard and span almost all industries in a variety of applications. One such application is the optimization of resource scheduling in a pay per use grid environment. Charging for these resources based on demand is often referred to as Utility Computing, where resource providers lease computing power with varying costs based on processing speed. Consumers using this resource have time and cost constraints associated with each job they submit. Determining the optimal way to divide the job among the available resources with regard to the time and cost constraints is tasked to the Grid Resource Broker (GRB). The GRB must use an optimization algorithm that returns an accurate result in a timely mam1er. The Genetic Algorithm and the Simulated Annealing algorithm can both be used to achieve this goal, although Simulated Annealing outperforms the Genetic Algorithm for use by the GRB. Determining optimal values for the variables used in each algorithm is often achieved through trial and error, and success depends upon the solution domain of the problem. Although this work outlines a specific grid resource allocation application, the results can be applied to any optimization problem based on dual constraints

Thermoluminescence of zircon: a kinetic model

The mineral zircon, ZrSiO4, belongs to a class of promising materials for geochronometry by means of thermoluminescence (TL) dating. The development of a reliable and reproducible method for TL dating with zircon requires detailed knowledge of the processes taking place during exposure to ionizing radiation, long-term storage, annealing at moderate temperatures and heating at a constant rate (TL measurements). To understand these processes one needs a kinetic model of TL. This paper is devoted to the construction of such amodel. The goal is to study the qualitative behaviour of the system and to determine the parameters and processes controlling TL phenomena of zircon. The model considers the following processes: (i) Filling of electron and hole traps at the excitation stage as a function of the dose rate and the dose for both (low dose rate) natural and (high dose rate) laboratory irradiation. (ii) Time dependence of TL fading in samples irradiated under laboratory conditions. (iii) Short time annealing at a given temperature. (iv) Heating of the irradiated sample to simulate TL experiments both after laboratory and natural irradiation. The input parameters of the model, such as the types and concentrations of the TL centres and the energy distributions of the hole and electron traps, were obtained by analysing the experimental data on fading of the TL-emission spectra of samples from different geological locations. Electron paramagnetic resonance (EPR) data were used to establish the nature of the TL centres. Glow curves and 3D TL emission spectra are simulated and compared with the experimental data on time-dependent TL fading. The saturation and annealing behaviour of filled trap concentrations has been considered in the framework of the proposed kinetic model and comparedwith the EPR data associated with the rare-earth ions Tb3+ and Dy3+, which play a crucial role as hole traps and recombination centres. Inaddition, the behaviour of some of the SiOmn− centres has been compared with simulation results.

Quantum Annealing - Foundations and Frontiers

We briefly review various computational methods for the solution of optimization problems. First, several classical methods such as Metropolis algorithm and simulated annealing are discussed. We continue with a description of quantum methods, namely adiabatic quantum computation and quantum annealing. Next, the new D-Wave computer and the recent progress in the field claimed by the D-Wave group are discussed. We present a set of criteria which could help in testing the quantum features of these computers. We conclude with a list of considerations with regard to future research.Comment: 22 pages, 6 figures. EPJ-ST Discussion and Debate Issue: Quantum Annealing: The fastest route to large scale quantum computation?, Eds. A. Das, S. Suzuki (2014

IMPLEMENTASI ALGORITMA SIMULATED ANNEALING PADA MASALAH PENJADWALAN PERKULIAHAN (STUDI KASUS DEPARTEMEN PENDIDIKAN MATEMATIKA FPMIPA UPI)

Penelitian ini membahas tentang masalah penjadwalan perkuliahan di Departemen Pendidikan Matematika FPMIPA Universitas Pendidikan Matematika. Penjadwalan perkuliahan (University Course Timetabling Problem (UCTP)) merupakan salah satu masalah optimisasi kombinatorial yang sulit diselesaikan menggunakan metode konvesional karena kompleksitasnya dan termasuk NP-Hard Problem (Nondeterministic Polynomial Time). Pada penelitian ini penulis mengimplementasikan algoritma Simulated Annealing untuk menyelesaikan masalah tersebut. Simulated Annealing merupakan salah satu algoitma percarian lokal (metaheuristic) bersifat generik yang mengadopsi proses pendinginan cairan logam hingga akhirnya menjadi kristal atau disebut annealing. Hasil penelitian menunjukkan bahwa Algoritma Simulated Annealing dapat diimplementasikan pada masalah penjadwalan perkuliahan di Departemen Pendidkan Matematika FPMIPA Universitas Pendidikan Indonesia dan mendapatkan solusi layak yang optimal karena memenuhi seluruh hard constraint dan soft constraint. This research discuss about university course timetabling problem at Department of Mathematics Education, Faculty of Mathematics and Natural Science Education, Indonesia. University course timetabling problem (UCTP) is a combinatorial optimization problem that is hard to solve using conventional methods because of its complexity and because it is a NP-Hard Problem (Nondeterministic Polynomial Time). In this research, we use Simulated Annealing Algorithm to solve the problem. Simulated Annealing is a local search (metaheuristic) algorithm that adopts the cooling process of a molten metal until finally it becomes a crystal. The reseach result show that Simulated Annealing Algorithm can be implemented in the university course timetabling problem at Department of Mathematics Education, Faculty of Mathematics and Natural Science Education, Indonesia University of Education and get an optimal feasible solution because it meets all the hard constraints and soft constraints