Results 41 to 50 of about 2,015,900 (233)

A New Spectral Conjugate Gradient method for solving unconstrained Optimization problems [PDF]

مجلة ابحاث كلية التربية الاساسية, 2022
The spectral conjugate gradient methods are fascinating, and it has been shown that they are useful for strictly convex quadratic reduction when used properly.
أسامة محمد طاهر ویس, خلیل خضر عبو, ابراهیم احمد صالح   +2 more
doaj   +1 more source

A modified quadratic hybridization of Polak-Ribiere-Polyak and Fletcher-Reeves conjugate gradient method for unconstrained optimization problems [PDF]

, 2017
This article presents a modified quadratic hybridization of the Polak–Ribiere–Polyak and Fletcher–Reeves conjugate gradient method for solving unconstrained optimization problems.
M.V. Thuto, Sindhu Narayanan, Pro Kaelo
core   +1 more source

A METHOD FOR SOLVING THE PROCUREMENT OPTIMIZATION PROBLEM BASED ON INVERSE CALCULATIONS [PDF]

Proceedings on Engineering Sciences, 2020
The paper describes a method for solving the procurement optimization problem based on inverse calculations. The method involves solving the unconstrained optimization problem and adjusting the obtained values of arguments subject to the constraint ...
Ekaterina Gribanova
doaj   +1 more source

Solving the Traveling Salesman Problem on the D-Wave Quantum Computer

Frontiers in Physics, 2021
The traveling salesman problem is a well-known NP-hard problem in combinatorial optimization. This paper shows how to solve it on an Ising Hamiltonian based quantum annealer by casting it as a quadratic unconstrained binary optimization (QUBO) problem ...
Siddharth Jain
doaj   +1 more source

Power Network Optimization: A Quantum Approach

IEEE Access, 2023
Optimization of electricity surplus is a crucial element for transmission power networks since it leads to reducing costs as well as increasing efficiency across the network as a whole.
Giuseppe Colucci, Stan van der Linde, Frank Phillipson   +2 more
doaj   +1 more source

Multi-Objective Optimization Technique Based on QUBO and an Ising Machine

IEEE Access
With an increase in the complexity of society, solving multi-objective optimization problems (MOPs) has become crucial. In this study, we introduced a novel method called “quadratic unconstrained binary optimization based on the weighted normal ...
Hiroshi Ikeda, Takashi Yamazaki
doaj   +1 more source

Intrinsic Phonons as a Dynamic Control Knob for Catalytic Reactivity in 2D Materials

Advanced Science, EarlyView.
Intrinsic phonon excitations are demonstrated to serve as a dynamic control knob for catalytic reactivity in 2D materials. Mode‐selective lattice vibrations reconstruct the electronic structure, lower oxygen evolution reaction barriers, establishing a phonon‐engineering paradigm for dynamically regulating electrocatalysis beyond conventional static ...
Kai Ren, Feifan Wang, Yong‐Wei Zhang, Tianyang Liu, Liangzhi Kou, Yu Jing   +5 more
wiley   +1 more source

Evaluating the Performance of Direct Higher-Order Formulations in Combinatorial Optimization Under Simulated Annealing

IEEE Access
Ising machines, including quantum annealing machines, are promising next-generation computers for combinatorial optimization problems. However, due to hardware limitations, most Ising-type hardware can only solve objective functions expressed in linear ...
Kazuki Ikeuchi, Yoshiki Matsuda, Shu Tanaka   +2 more
doaj   +1 more source

Highly‐Uniform Passive Crossbar Arrays of Resistive Switching Random Access Memory (RRAM) for In‐Memory Computing Applications

Advanced Electronic Materials, EarlyView.
Passive resistive memory arrays promise efficient in‐memory computing but suffer from sneak paths and programming variability. Here, highly uniform 32 × 32 passive RRAM crossbars are programmed with multilevel precision below 3% error and 99.5% yield.
S. Ricci, P. Mannocci, M. Porzani, D. Bridarolli, F. Carletti, M. Farronato, D. Ielmini   +6 more
wiley   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source