• To effectively solve the problem, a novel parallel-series hybrid meta-heuristic optimisation method is then proposed, which combines a hybrid topology binary particle swarm optimisation, the self-adaptive differential evolution algorithm and a lambda iteration method, to simultaneously and intelligently determine the binary on/off status of each thermal unit, the generation power of online units, as well as the demand side management of plug-in electric vehicles. (whiterose.ac.uk)
  • Karmarkar and Karp present an algorithm that overcomes this problem. (wikipedia.org)
  • We also present a deterministic fixed-parameter algorithm for Bin Covering with run time O((k! (dagstuhl.de)
  • A binary generalized differential evolution algorithm (BGDE) is inspired by the novel modified binary differential evolution algorithm (NMBDE) for single-objective optimization problems and GDE3. (tuhh.de)
  • The BGDE and multi-objective binary probability optimization algorithm (MBPOA), which is developed for binary-coded problems, are implemented in a compiler framework for hard real-time systems to perform an automatic minimization of the code size and WCET for the well-known compiler optimization function inlining. (tuhh.de)
  • Algorithm solving combinatorial problem of optimal design based on binary partitioning, a parametric search and dynamic programming optimization of a binary tree is described and demonstrated in numeric example. (scirp.org)
  • A. Chaves and L. Lorena, "Clustering Search Algorithm for the Capacitated Centered Clustering Problem," Computers and Operations Research, Vol. 37, No. 3, March 2010, pp. 552-558. (scirp.org)
  • P. McGregor and D. Shen, (1977) "Network Design: An Algorithm for the Access Facility Location Problem," IEEE Transactions on Communications, Vol. COM-25, No. 1, January 1977, pp. 61-73. (scirp.org)
  • Gumus and Ciric [7] use a sequential approximation algorithm and Sahin and Ciric [8], a dual temperature simulated annealing approach to solve the BLP design problem with embedded phase equilibrium. (aiche.org)
  • Further, we derive another algorithm, TotalQBoost, as a theoretically motivated totally corrective boosting algorithm with cardinality penalization that also makes use of quantum optimization. (purdue.edu)
  • The procedure involves four main tools: the multi-objective particle swarm optimization method (MOPSO) to find Pareto solutions in one run, the K-means clustering algorithm to reduce the size of the obtained non-dominated solutions, the pseudo-weight vector approach (PWV) to facilitate the decision making and to select some adequate solutions, and finally, CFD simulations to analyze the retained optimal solutions. (iwaponline.com)
  • The case study is solved using a novel Discrete Binary Gaining Sharing Knowledge-based Optimization algorithm (DBGSK), which involves two main stages: discrete binary junior and senior gaining and sharing stages. (techscience.com)
  • Due to complexity of the optimization criteria, taking into consideration both the number of equations and the number of variables on two stages of solution -- solving a system for each clan and solving a composition system -- simplified variants of the task are considered and solved using heuristic techniques: a fast bin packing with the first fit on a sorted array algorithm and a multi-objective graph partitioning with software package METIS. (lvee.org)
  • We develop an iterated tabu search (ITS) algorithm for solving this problem. (hindawi.com)
  • A mean ®eld feedback arti®cial neural network (ANN) algorithm is developed and explored for the set covering problem. (lu.se)
  • Arbitrary problems can be processed using the algorithm via a public domain server. (lu.se)
  • Their typical similarity metric is modified to a weighted Euclidean metric and automatically adjusted by a genetic algorithm, a heuristic search (optimization) technique. (bvsalud.org)
  • The configuration linear program (configuration-LP) is a linear programming technique used for solving combinatorial optimization problems. (wikipedia.org)
  • Combinatorial optimization problems are one of the target applications of current quantum technology, mainly because of their industrial relevance, the difficulty of solving large instances of them classically, and their equivalence to Ising Hamiltonians using the quadratic unconstrained binary optimization (QUBO) formulation. (arxiv.org)
  • Those problems have been studied extensively from the viewpoint of approximation algorithms, but their parameterized complexity has only been investigated barely. (dagstuhl.de)
  • For problem instances containing no 'small' items, classical matching algorithms yield optimal solutions in polynomial time. (dagstuhl.de)
  • Our main results are fixed-parameter algorithms for vector versions of Bin Packing, Multiple Knapsack, and Bin Covering parameterized by k. (dagstuhl.de)
  • We study a binary quantum control optimization problem, where control decisions are binary-valued and the problem is solved in diverse quantum algorithms. (optimization-online.org)
  • This breakthrough in mathematical principles helps solve problems related to hyperdimensional binary variable optimization and makes it possible to both conduct deep learning algorithms in the Boolean field and realize the performance of a full-precision model through simulated verification. (huawei.com)
  • The platform is an automated judgement system that enables the comparison of algorithms for different problems (such as, e.g., the Travel Salesman Problem, the Job Shop Scheduling Problem, among others). (hsu-hh.de)
  • Finally, the Genetic Algorithms optimization method is used to obtain the optimal grinding process parameters. (ncl.edu.tw)
  • Unit commitment is a traditional mixed-integer non-convex problem and remains a key optimisation task in power system scheduling. (whiterose.ac.uk)
  • This category is reserved for problems that depend critically on the mixed-integer capabilities of CVX. (cvxr.com)
  • The high penetration of intermittent renewable generations such as wind and solar as well as mass roll-out of plug-in electric vehicles (PEVs) impose significant challenges to the traditional unit commitment problem, not only by significantly increasing the complexity of the problem in terms of the dimension and constraints, but also dramatically change the problem formulation. (whiterose.ac.uk)
  • Moreover, even the problem itself is generally very large: it has C variables and S constraints. (wikipedia.org)
  • A linear program with no integrality constraints can be solved in time polynomial in the number of variables and constraints. (wikipedia.org)
  • In this paper we discuss a class of primal heuristics that are based on a reformulation of the problem as a mathematical program with equilibrium constraints. (optimization-online.org)
  • The decision variables are binary ones that represent whether to choose a specific consumer, and design constraints are formulated to keep track of the chosen route. (techscience.com)
  • To better illustrate the problem, objective, and problem constraints, a real application case study is presented. (techscience.com)
  • We consider free time optimal control problems with pointwise set control constraints u(t) ∈ U(t). (uchile.cl)
  • In 2022, QCi applied its initial quantum computer prototype to the BMW sensor problem delivering a superior, feasible solution to a real-world problem. (quantumcomputinginc.com)
  • In Optimization Methods and Software , 2022. (cnam.fr)
  • Usually, the number of configurations is exponential in the problem size, but in some cases it is possible to attain approximate solutions using only a polynomial number of configurations. (wikipedia.org)
  • It reduces considerably the number of iterations changing this NP hard problem into a polynomial search one. (arxiv.org)
  • Title: Multi-Objective Optimization for the Compiler of Hard Real-Time Systems. (tuhh.de)
  • Learning to solve bin packing problems with an immune inspired hyper-heuristic. (napier.ac.uk)
  • Although the original problem assumes centralized decision-making, it's interesting that the heuristic allows for distributed decisions, which is more practical. (lumina.com)
  • Given the practical importance of the problem, many methods, both exact and heuristic, have been proposed in the literature. (hindawi.com)
  • In this paper we approach them by their distance from triviality, measuring the problem complexity by the number k of small items. (dagstuhl.de)
  • Since the computational complexity of solving Diophantine systems is higher than for systems over real numbers, methods to speed-up the process are in demand. (lvee.org)
  • The optimization criterion should take into consideration the complexity of solving systems for clans and solving the composition system. (lvee.org)
  • In addition, we describe several approaches to practically compute solving sequences and lower bounds despite the high combinatorial complexity of the problem. (aaai.org)
  • We study the problem of supervised binary classification from the perspective of deploying adiabatic quantum optimization in training. (purdue.edu)
  • Given our QUBO formulation of this NP-hard problem, it should be possible to solve it on adiabatic quantum computers. (fraunhofer.de)
  • I am aware that it's possible to use QAOA to solve QUBO problems. (stackexchange.com)
  • While I understand that quadratization from HOBO/HUBO to QUBO could be done [1] [2] , I'm trying to understand if it's actually possible to solve problems with higher-order polynomials using QAOA without quadratizing them. (stackexchange.com)
  • We are concerned with k-medoids clustering and propose aquadratic unconstrained binary optimization (QUBO) formulation of the problem of identifying k medoids among n data points without having to cluster the data. (fraunhofer.de)
  • In the field of network optimization, we created a topology-aware network pricing policy (NPP) model and a self-adaptive sparse optimization method to solve problems surrounding flows in networks that possess hundreds of thousands of nodes and tens of millions of links, improving problem-solving speeds by orders of magnitude. (huawei.com)
  • Both pros and cons of such PF-type optimization methods will be discussed. (cam.ac.uk)
  • His scientific interests span from Bayesian inference (especially Monte Carlo methods), statistical modeling, machine learning to stochastic optimization. (cam.ac.uk)
  • M. Bischoff and K. D?chert, "Allocation Search Methods for a Generalized Class of Location-Allocation Problems," European Journal of Operational Research, Vol. 192, No. 3, February 2009, pp. 793-807. (scirp.org)
  • Herein, we rigorously solve design problems with deterministic global methods in the sense that the necessary and sufficient criteria of phase stability are satisfied and present a numerical study and a best practice to solve such programs efficiently. (aiche.org)
  • Solving linear systems is a central task of numerical methods. (lvee.org)
  • In Journal of Global Optimization , 80 (2): 231-248, 2021. (cnam.fr)
  • This is largely a system design problem to be solved, and naturally, Operations Research leads the way. (lumina.com)
  • This problem is very hard, and so the researchers applied a genetic programming approach to find good solutions to the problem. (lumina.com)
  • Here we present an alternative approach that greatly reduces the requirements for coherent evolution and combine this method with a new approach to state preparation based on ansätze and classical optimization. (nature.com)
  • The approach is numerically tested against a set of publicly available test problems with sizes ranging up to 5 Â 103 rows and 106 columns. (lu.se)
  • Most of obtained by, e.g., a branch-and-bound approach the activities have been directed towards feed-for- for modestly sized problems. (lu.se)
  • We apply QBoost and TotalQBoost to three different real-world computer vision problems and make use of a quantum processor for solving the sequence of discrete optimization problems generated by one of them. (purdue.edu)
  • We describe a series of increasingly complex designs that result in computationally hard training problems of combinatorial nature. (purdue.edu)
  • For compatibility with quantum hardware we derive the corresponding quadratic binary problem via variational approximation. (purdue.edu)
  • The set covering problem (SCP) is a well- approximation to the thermodynamics of spin known NP-hard combinatorial optimization systems. (lu.se)
  • For larger problems ward architectures for pattern recognition or various approximative schemes have been sug- function approximation. (lu.se)
  • A nonlinear binary mathematical programming model for the problem is formulated. (techscience.com)
  • Augmented Lagrangian method, Inequality constrained, Nonlinear min-max optimization problems. (edu.et)
  • This course provides instruction on the uses of Excel Solver to solve complex business problems. (cpethink.com)
  • Instead of classical binary bits, quantum computers use qubits - the basic units for processing information and speed up the process of solving complex computations. (computer.org)
  • QCi's Entropy Quantum Computer is uniquely designed to solve for this noise and create useful qubits to perform computations now. (quantumcomputinginc.com)
  • In practice, this means that when attempting to solve a problem that has several variables, the computations needed to run these correlations grows exponentially. (computerweekly.com)
  • We reformulate the BLP as a semi-infinite program, which we then solve using the method of Blankenship and Falk [9]. (aiche.org)
  • The experimental results based on ten high-dimensional microarray classification problems demonstrated the effectiveness of our proposed method. (hindawi.com)
  • We apply the method to the problem of tuning the learning rate when solving linear regression problems and to the optimization of the HPs of XGBoost binary classifiers across different datasets, showing that we favorably compare with recently proposed extensions of Hyperband. (amazon.science)
  • This work tests the unbalanced penalization method using real quantum hardware on D-Wave Advantage for the traveling salesman problem (TSP). (arxiv.org)
  • The results show that the unbalanced penalization method outperforms the solutions found using slack variables and sets a new record for the largest TSP solved with quantum technology. (arxiv.org)
  • OCA brings substantial differences and improvements compared to previous coordinate ascent feature selection method: we group variables into block and individual variables instead of a binary selection. (arxiv.org)
  • The values of an attribute are represented by a binary equivalence relation. (hindawi.com)
  • J. Zhou and B. Liu, "Modeling Capacitated Location-Allocation Problem with Fuzzy Demands," Computers and Industrial Engineering, Vol. 53, No. 3, October 2007, pp. 454-468. (scirp.org)
  • This paper investigates a critical resource allocation problem in the fi. (deepai.org)
  • ANN is a computer paradigm that has gained a source allocation problems. (lu.se)
  • Sophisticated compilers provide various optimizations to improve code aggressively w.r.t. different objective functions, e.g., worst-case execution time (WCET) and code size, which usually contradict each other. (tuhh.de)
  • In this work the multiarea optimal power flow (OPF) problem is decoupled into areas creating a set of regional OPF subproblems. (uchile.cl)
  • Then, the ILP can be solved either by complete search (if S, C are sufficiently small), or by relaxing it into a fractional LP. The fractional configuration LP of bin-packing It is the linear programming relaxation of the above ILP. (wikipedia.org)
  • Varying minimal clan size brings in certain imbalance when solving a linear (Diophantine) system on parallel architectures via composition of its clans using open source software PaAd. (lvee.org)
  • The technique for the composition of linear system clans 1 has been further developed with regard to modern parallel architectures and applied to speed-up solving systems of linear Diophantine equations 2 , open source software package ParAd released 3 . (lvee.org)
  • Staircase structures play an important role in many optimization problems involving linear programs. (utwente.nl)
  • It has become apparent that systems with this characteristic structure can be solved in linear time, as opposed to standard linear programs which are usually less efficient. (utwente.nl)
  • The simultaneous maze solving problem asks for the shortest movement sequence starting in the upper left corner and visiting the lower right corner for all mazes of size n _ m (for which a path from the upper left to the lower right corner exists at all). (aaai.org)
  • We present a theoretical problem analysis, including hardness results and a cubic upper bound on the sequence length. (aaai.org)
  • While Hyperband is conceptually simple, combining random search to a successive halving technique to reallocate resources to the most promising HPs, it often outperforms standard Bayesian optimization when solutions with moderate precision are sufficient. (amazon.science)
  • The coordinate ascent optimization solves the issue of the NP hard original problem where the number of combinations rapidly explode making a grid search unfeasible. (arxiv.org)
  • Then, the configuration LP of bin-packing is: minimize ∑ c ∈ C x c subject to {\displaystyle {\text{minimize}}~~~\sum _{c\in C}x_{c}~~~{\text{subject to}}} ∑ c ∈ C a s , c x c ≥ n s {\displaystyle \sum _{c\in C}a_{s,c}x_{c}\geq n_{s}} for all s in S (- all ns items of size s are packed). (wikipedia.org)
  • The Volkswagen Group and the Amazon Quantum Solutions Lab used Quantum Approximate Optimization to minimize the number of paint swaps in a POC for a small number of vehicles with a random order of cars to paint. (amazon.com)
  • Potential applications of quantum computers are vast, range from cryptography and optimization to machine learning and materials science. (computer.org)
  • Sign-up for a free trial account for remote access to our Dirac quantum computers, and start working on your optimization problems. (quantumcomputinginc.com)
  • Processes problems with data sets that require higher performance than currently available quantum computers. (quantumcomputinginc.com)
  • Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. (nature.com)
  • How to model this constraint with binary variables in CVX? (cvxr.com)
  • 3) Identify how to use the binary constraint to make optimum choices among alternatives. (cpethink.com)
  • Given the abundance of NP-hard optimization problems that naturally arise in learning, it is clear that machine learning can immensely benefit from such an optimization tool. (purdue.edu)
  • My OpenOffice files are binary (zipped XML), my serialized pyxie trees (Python pickles) are binary. (xml.org)
  • This problem will most certainly hit you on Unix when using Python 2.4. (lu.se)
  • This problem has not been reported for Python 2.5+, so best to upgrade. (lu.se)
  • Nonconvex mixed-binary nonlinear optimization problems frequently appear in practice and are typically extremely hard to solve. (optimization-online.org)
  • For quantum systems, where the physical dimension grows exponentially, finding the eigenvalues of certain operators is one such intractable problem and remains a fundamental challenge. (nature.com)
  • However, because the dimension of the problem grows exponentially with the size of the physical system under consideration, exact treatment of these problems remains classically infeasible for compounds with more than 2-3 atoms 1 . (nature.com)
  • The problem is that the number of variables in the fractional configuration LP is equal to the number of possible configurations, which might be huge. (wikipedia.org)
  • OCA takes into account the notion of dependencies between variables forming blocks in our optimization. (arxiv.org)
  • N g is a set of binary spin variables s function and discuss the MF treatment. (lu.se)