Results 41 to 50 of about 2,364,396 (151)

A continuous reformulation of the quadratic unconstrained binary optimization problem [PDF]

, 2015
In this paper we consider the Quadratic Unconstrained Binary Optimization (QUBO) Problem. Using a suitable function and penalty parameter we can reformulate the original QUBO problem as a continuous program. It is shown that the problem of large size can
ZAPOROJAN, Sergiu, MORARU, Vasile
core   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design

Advanced Intelligent Discovery, EarlyView.
Active learning (AL) strategies are benchmarked for the binary design of planar multilayer nanophotonic structures. Factorization machines combined with quantum annealing (QA) become effective as dimensionality increases. Hybrid QA provides the strongest results for 100‐layer problems, highlighting the importance of optimization method selection in ...
Serang Jung, Seongmin Kim, Jaehyun Park, Chaeyoung Park, Zhihao Xu, Sanghyo Hwang, Baiqing Ye, Youn sang Kim, Sun‐Kyung Kim, Tengfei Luo, Eungkyu Lee   +10 more
wiley   +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source

A Scalable and Resource‐Efficient Pipelined p‐Computer for Probabilistic Ising Machines

Advanced Intelligent Systems, EarlyView.
(a) Block diagram of the portfolio optimization problem: given M assets, the goal is to determine the optimal weights w that maximize the expected return (based on the mean historical assets return u), while minimizing the risk, quantified by the assets covariance matrix S.
Deborah Volpe, Eleonora Raimondo, Andrea Grimaldi, Pedram Khalili Amiri, Stefano Chiappini, Anna Giordano, Mario Carpentieri, Hyunsoo Yang, Massimo Chiappini, Giovanni Finocchio   +9 more
wiley   +1 more source

Balancing Exploration and Exploitation Through a Sequential Hybrid of Roach Infestation and Mayfly Algorithms for Constrained Engineering Design Optimization

AI &Innovation, EarlyView.
ABSTRACT Maintaining an effective balance between exploration and exploitation is essential during optimization processes, from mathematical functions to more complex problems such as constrained engineering design optimization, particularly when addressing highly nonlinear issues with numerous local optima and strict feasibility requirements.
Enrique Lizárraga, Fevrier Valdez, Oscar Castillo, Patricia Melin   +3 more
wiley   +1 more source

Unconstrained formulation of standard quadratic optimization problems [PDF]

, 2010
A standard quadratic optimization problem (StQP) consists of nding the largest or smallest value of a (possibly indenite) quadratic form over the standard simplex which is the intersection of a hyperplane with the positive orthant.
Grippo, Luigi, Bomze, Immanuel, Palagi, Laura   +2 more
core  

Binary Unconstrained Quadratic Optimization Problem

, 2013
In recent years the unconstrained quadratic binary program (UQP) has emerged as a unified framework for modeling and solving a wide variety of combinatorial optimization problems.
Gary A. Kochenberger, Glover, Fred, Fred Glover, Wang, Haibo, Kochenberger, Gary A., Haibo Wang   +5 more
core   +1 more source

Anatomy and variation in the paraphyletic Mosasaurus lemonnieri assemblage from the Craie phosphatée de ciply, Belgium

The Anatomical Record, EarlyView.
Abstract Nineteenth century excavations of the Ciply‐Malogne Phosphatic Chalk Formation in Hainaut, Belgium, yielded over 50 partial mosasaur skeletons, the majority of which were referred to the species Mosasaurus lemonnieri Dollo, 1889. In the years since, this taxon has experienced a complicated taxonomic history, and its exceptional hypodigm has ...
Henry S. Sharpe, Hallie P. Street‐Sampson, Takuya Konishi, Michael W. Caldwell   +3 more
wiley   +1 more source

Performance improvement of discrete‐time linear‐quadratic regulators applied to uncertain linear systems using the Tikhonov regularization method

Asian Journal of Control, EarlyView.
Abstract The linear‐quadratic regulator (LQR) problem of optimal control of an uncertain discrete‐time linear system (DTLS) is revisited in this paper from the perspective of Tikhonov regularization. We show that an optimally chosen regularization parameter reduces, compared to the classical LQR, the values of a scalar error function, as well as the ...
Fernando Pazos, Amit Bhaya
wiley   +1 more source