Integer Quadratic(정수 2차)란 무엇입니까?
Integer Quadratic 정수 2차 - We compare our approach against a two-phase method computed using an optimization solver in both phases: it first computes the follower’s value function for all feasible leader’s decisions, and then solves a single-level, value-function reformulation of BQKP as a mixed-integer quadratically constrained program. [1] This work formulates the last-mile delivery problem assisted byUMCs as amixed-integer quadratically-constrained program (MIQCP) and develops a greedy heuristic solution, inspired by the decomposition algorithms for large-scale optimization. [2] The method amounts to solving a series of convex optimization problems in WDNs augmented by the LinDistFlow in PDNs-enablingto formulate the problem, which is a mixed-integer quadratically constrained quadratic program called C-OWPF. [3] To speed up the solution process, the original nonlinear/nonconvex operation constraints are reformulated to a mixed-integer quadratically constrained programming form by linearization/convexification and scenario generation/reduction methods. [4] We develop a mixed-integer quadratically constrained program to investigate the least cost system configuration and operation. [5] Thus, a mixed-integer quadratically-constrained program is presented that optimizes the operation of heat pumps in combination with thermal energy storages and the operating temperatures of a pipe network. [6] For networks with nonoverlapping cycles, a provably exact convex relaxation of the pressure drop equations yields a mixed-integer quadratically constrained quadratic program (MI-QCQP) solver. [7] GUROBI, one of the prominent MIP solvers, recently added the capability to solve mixed-integer quadratically-constrained quadratic programs (MIQCQPs). [8] The class of mixed-integer quadratically constrained quadratic programs (QCQP) consists of minimizing a quadratic function under quadratic constraints where the variables could be integer or continuous. [9] These relaxations constitute a key component for some methods for solving nonconvex mixed-integer quadratically constrained quadratic programming (MIQCQP) problems. [10] In this paper, a novel MIQCP (mixed-integer quadratically constrained programming) model has been developed for the optimal scheduling of such parallel and performance-decaying unit system with consideration of inherent upset reduction. [11] By applying convex relaxation and introducing auxiliary variables, the model is converted to a mixed-integer quadratically constrained programming model that can be effectively solved. [12] This paper will examine the solution of the minimum loss reconfiguration problem of distribution networks that determine the optimal switches, by means of a mixed-integer quadratically-constrained programming (MIQCP) model The proposed MIQCP model is a convex formulation, which allows to find the optimal solution using optimization solvers. [13] We formulate a Mixed-Integer Quadratically Constrained Programming (MIQCP) problem that can simultaneously determine the optimal placement and daily activation/deactivation schedules of each RSU, whether or not they have a solar panel attached, and their ranges during each period of time. [14]두 단계에서 최적화 솔버를 사용하여 계산된 2단계 방법과 우리의 접근 방식을 비교합니다. 먼저 모든 실행 가능한 리더의 결정에 대해 추종자의 가치 함수를 계산한 다음 BQKP의 단일 수준, 가치-함수 재구성을 혼합형으로 해결합니다. 정수 2차 제약 프로그램. [1] 이 작업은 혼합 정수 2차 제약 프로그램(MIQCP)으로 UMC가 지원하는 라스트 마일 전달 문제를 공식화하고 대규모 최적화를 위한 분해 알고리즘에서 영감을 받은 탐욕적 휴리스틱 솔루션을 개발합니다. [2] 이 방법은 C-OWPF라는 혼합 정수 2차 제약 2차 프로그램인 문제를 공식화하기 위해 PDN의 LinDistFlow에 의해 증대된 WDN의 일련의 볼록 최적화 문제를 해결하는 것과 같습니다. [3] 솔루션 프로세스의 속도를 높이기 위해 원래의 비선형/비볼록 연산 제약 조건은 선형화/볼록화 및 시나리오 생성/축소 방법을 통해 혼합 정수 2차 제약 프로그래밍 형식으로 다시 공식화됩니다. [4] 최소 비용 시스템 구성 및 작동을 조사하기 위해 혼합 정수 2차 제약 조건이 있는 프로그램을 개발합니다. [5] 따라서 열 에너지 저장 및 파이프 네트워크의 작동 온도와 함께 히트 펌프의 작동을 최적화하는 혼합 정수 2차 구속 프로그램이 제공됩니다. [6] 겹치지 않는 주기가 있는 네트워크의 경우 압력 강하 방정식의 정확한 볼록 완화는 혼합 정수 MI-QCQP(2차 제약 2차 계획법) 솔버를 생성합니다. [7] 저명한 MIP 솔버 중 하나인 GUROBI는 최근 혼합 정수 2차 제약 2차 계획법(MIQCQP)을 푸는 기능을 추가했습니다. [8] 혼합 정수 2차 제약 2차 계획법(QCQP) 클래스는 변수가 정수 또는 연속일 수 있는 2차 제약 조건에서 2차 함수를 최소화하는 것으로 구성됩니다. [9] 이러한 완화는 볼록하지 않은 혼합 정수 2차 제약 2차 계획법(MIQCQP) 문제를 해결하기 위한 일부 방법의 핵심 구성요소를 구성합니다. [10] 본 논문에서는 이러한 병렬 및 성능 저하 단위 시스템의 고유한 업셋 감소를 고려하여 최적의 스케줄링을 위해 새로운 MIQCP(혼합 정수 2차 제약 프로그래밍) 모델을 개발했습니다. [11] 볼록 완화를 적용하고 보조 변수를 도입하여 모델을 효과적으로 풀 수 있는 혼합 정수 2차 제약 계획법 모델로 변환합니다. [12] 본 논문에서는 최적의 스위치를 결정하는 배전망의 최소 손실 재구성 문제를 MIQCP(Mixed Integer Quadratically-Constrained Programming) 모델을 통해 해결하는 방법을 검토합니다. 제안된 MIQCP 모델은 볼록 공식으로 다음을 찾을 수 있습니다. 최적화 솔버를 사용한 최적의 솔루션. [13] 각 RSU의 최적 배치 및 일일 활성화/비활성화 일정, 태양광 패널 부착 여부 및 각 기간 동안의 범위를 동시에 결정할 수 있는 MIQCP(Mixed-Integer Quadratically Constrained Programming) 문제를 공식화합니다. [14]
mixed integer linear 혼합 정수 선형
Then, the problem of identifying real barrier certificates from candidates is transformed into a group of mixed integer linear programming problems and a mixed integer quadratically constrained problem. [1] Most of the existing methods for the problem are based on first converting the OTS into a mixed-integer linear program (MILP) or mixed-integer quadratic program (MIQP), and then iteratively solving a series of its convex relaxations. [2] We cast our problem as mixed integer linear programming (MILP) formulation for uniform transmit power and mixed integer quadratic constraint programming (MIQCP) for power allocation and compare between both methods. [3] To optimize the performance of the two tasks, a mixed-integer linear programming (MILP) model and a mixed-integer quadratic programming (MIQP) model are proposed, respectively. [4] The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem. [5] Additionally, we compare a Mixed Integer Linear Programming (MILP) approach to Mixed Integer Quadratic Constrained Programming (MIQCP). [6] Consequently, two optimization models – a fuzzy mixed-integer linear programming model and a fuzzy mixed-integer quadratic programming model – were developed in this study. [7]그런 다음 후보자에서 실제 장벽 인증서를 식별하는 문제는 혼합 정수 선형 계획법 문제와 혼합 정수 2차 제약 문제 그룹으로 변환됩니다. [1] 문제에 대한 기존 방법의 대부분은 먼저 OTS를 혼합 정수 선형 계획법(MILP) 또는 혼합 정수 2차 계획법(MIQP)으로 변환한 다음 일련의 볼록 완화를 반복적으로 푸는 방식을 기반으로 합니다. [2] 균일한 전송 전력을 위한 혼합 정수 선형 계획법(MILP) 공식과 전력 할당을 위한 혼합 정수 2차 제약 계획법(MIQCP)으로 문제를 던지고 두 방법을 비교합니다. [3] nan [4] nan [5] 또한 혼합 정수 선형 계획법(MILP) 접근 방식을 혼합 정수 2차 제약 계획법(MIQCP)과 비교합니다. [6] nan [7]
Mixed Integer Quadratic 혼합 정수 2차
We develop an asymptotically valid randomization-based testing procedure for this new estimand based on solving a mixed integer quadratically-constrained optimization problem. [1] Firstly, a mixed integer quadratic programming (MIQP) model is formulated to describe unit commitment problems (UCPs) for each microgrid (MG) and energy transactions among MMGs. [2] We model the problem as a mixed integer quadratic program and linearize it. [3] Secondly, the nonlinear terms in the original optimization equation are eliminated, and it is converted into a mixed integer quadratic programming(MIQP) problem so that it can be solved using the standard method. [4] By using the mixed integer quadratic programming (MIQP), a joint spatial-temporal resource allocation scheme is obtained. [5] Then, the proposed bilevel model is transformed into a mixed integer quadratic programming model using duality theory and Karush-Kuhn-Tucker conditions. [6] As the proposed control algorithm presents both continuous and binary variables, its associated optimization problem is formulated using the Mixed Logic Dynamic (MLD) framework, which results in a Mixed Integer Quadratic Programming problem (MIQP). [7] Conditional Value at Risk (CVaR) based mixed integer quadratic programming formulation is built to hedge the risks of random charging demands and volatile market prices. [8] The problem is solved offline as a mixed integer quadratic program, which generates trajectories for the velocity and for freewheeling. [9] The economic dispatch of a CCHP microgrid with AA-CAES is then modeled as mixed integer quadratic programming (MIQP). [10] In this paper, a mixed integer quadratic programming (MIQCP) based post-event distribution restoration framework in response to high impact low probable events is propounded to maintain the power supply continuity of critical loads particularly in emergency conditions. [11] Firstly, the centroid change curve is drawn based on greedy strategy and mixed integer quadratic programming in this study, then the strategic optimization calculation model that can accurately describe the oil supply problem is established. [12] The second-order cone relaxation method and the incremental formulation for the piecewise linearization method are utilized to transform the proposed nonlinear model into mixed integer quadratic constraint programming model, which can be effectively solved by the commercial solver. [13] The mixed integer quadratic programming (MIQP) is used for sub-problem modeling. [14] This work presents a Mixed Integer Quadratically Constrained Program with temperature constraints. [15] The model is a mixed integer quadratic programming problem, which is solved by Gurobi. [16] Then, the problem of identifying real barrier certificates from candidates is transformed into a group of mixed integer linear programming problems and a mixed integer quadratically constrained problem. [17] Given an application description, which includes some performance description and quality of service requirements, the PMS system selects a subset of the global graph by solving a mixed integer quadratically constrained programming formulation, which finds the appropriate set of nodes for optimal application execution performance. [18] We formulate the trajectory planning problem as Mixed Integer Quadratic Programming (MIQP) with a B-spline representation. [19] Preliminary results with application to mixed integer quadratic indefinite optimization further reveal the performance superiority of the proposed methodology relative to the standard techniques. [20] The energy trading of IMOs is formulated as a mixed integer quadratic programming (MIQP) in the outer level. [21] Then, to reduce the difficulty of calculation, the original problem is simplified into a series of nested mixed integer quadratic programming problems by using forward selection strategy. [22] Since the original problem is non-solvable in optimization solvers due to unsupported types of quadratic constraints, it is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. [23] Specifically, we develop a mixed integer quadratic programming to determine the optimal size and schedules of a grid-scale ESS in an Australian distribution network. [24] A reformulation method is presented which results in a mixed integer quadratic program by introducing binary activation variables. [25] This paper develops a multi-objective mixed integer quadratic programming (MMIQP) model to optimize the design and planning of an algae-based biofuel supply chain network under environmental and economic objectives. [26] We cast our problem as mixed integer linear programming (MILP) formulation for uniform transmit power and mixed integer quadratic constraint programming (MIQCP) for power allocation and compare between both methods. [27] The proposed mixed integer quadratic model consists of associations rules related to a customer’s consumption pattern, capacity of the items, and stores both the distance among items and staging area. [28] The unit commitment problem is solved using Mixed Integer Quadratic Programming (MIQP). [29] The optimization problem is formulated as a Mixed Integer Quadratic Problem and uses the different power references as levers to reach this optimum and maintain the state of the microgrid within limitations. [30] In this paper, we propose mixed integer quadratic optimization (MIQO) formulations for selecting the best subset of explanatory variables subject to the upper bounds on the VIFs of selected variables. [31] We present a mixed integer quadratic programming model for determining correct dosage given characteristics of a patient. [32] We formulate it as a mixed integer quadratic programming problem (MIQP), which allows cell spreading concurrently in both the horizontal and vertical directions. [33] In outer layer, the x -update and v -update steps can be decoupled for each unit and executed in parallel after rearranging and grouping the variables and constraints of the UC mixed integer quadratic programming (MIQP) model according to each unit. [34] In this research, a robust mixed integer quadratic programming (RMIQP) model is proposed to determine the number of allocated canisters and the schedule of replenishment operations considering multiple scenarios. [35] The optimization tool commits and dispatches generating units while simultaneously determining the geographical procurement of the required spinning reserve as well as load-following ramping reserve, by mixed integer quadratic programming (MIQP). [36] The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem. [37] Additional consideration of energy efficiency aspects leads to a Mixed Integer Quadratically Constraint Program (MIQCP), which is solved by substitution of Dijkstra’s algorithm by a branch and bound method. [38] Furthermore, we present a Mixed Integer Quadratic Program formulation in which the solver can choose the trajectory interval allocation, and where a time allocation heuristic is computed efficiently using the result of the previous replanning iteration. [39] In the first stage, the initial switch combinations of LSs and DERs’ scheduling are obtained through a mixed integer quadratic programming, whereas the second stage is based on rule-based power management algorithm. [40] The proposed control, leveraging a mixed integer quadratic programming problem, allows to meet manifold thermal and electric users’ requirements and react to inbound demand response signals, while still guaranteeing stable operation of the building's electric and thermal storage equipment. [41] This paper presents mixed integer quadratic programming-based scheduling methods for both DA market bidding and ID operation of VPPs, thus serving as a complete scheme for bidding-operation scheduling. [42] We propose mixed integer quadratic and linear programming (resp. [43] The proposed EMS models are mixed integer quadratic programming problems, requiring less computation time and thus suitable for online applications. [44] The first method considered in this study is a mixed integer quadratic program MPC based on mixed linear dynamic formulations. [45] We adapt three distinct optimization strategies – mixed integer quadratic programming, simulation-based genetic algorithm and expert-based heuristic – and empirically compare their strengths. [46] In this paper, by adopting an in-house developed simulation tool (©E-OPT) based on mixed integer quadratic programming, a sensitivity analysis has been carried out for investigating the techno-economic impact of different storage technologies (i. [47] The formulated optimization problem can be classified as a convex Mixed Integer Quadratic Program; consequently, its solution can be found using readily available generic solvers. [48] The mathematical model is of type mixed integer quadratically constrained quadratic programming and its characteristics are analyzed. [49] In this paper, proposed sine cosine algorithm searches allocation of generators (units that participate in generation to take upload) and once units are decided, allocation of generations (economic load dispatch) is done by mixed integer quadratic programming. [50]혼합 정수 2차 제약 조건이 있는 최적화 문제 해결을 기반으로 이 새로운 추정량에 대해 점근적으로 유효한 무작위화 기반 테스트 절차를 개발합니다. [1] 첫째, 혼합 정수 2차 계획법(MIQP) 모델은 각 마이크로그리드(MG)에 대한 단위 확약 문제(UCP)와 MMG 간의 에너지 거래를 설명하기 위해 공식화됩니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] 제안된 비선형 모델을 상용 솔버로 효과적으로 해결할 수 있는 혼합 정수 2차 제약 조건 프로그래밍 모델로 변환하기 위해 2차 원뿔 이완 방법과 조각별 선형화 방법에 대한 증분 공식이 활용됩니다. [13] nan [14] nan [15] nan [16] 그런 다음 후보자에서 실제 장벽 인증서를 식별하는 문제는 혼합 정수 선형 계획법 문제와 혼합 정수 2차 제약 문제 그룹으로 변환됩니다. [17] 일부 성능 설명 및 서비스 품질 요구 사항을 포함하는 응용 프로그램 설명이 주어지면 PMS 시스템은 최적의 응용 프로그램 실행 성능을 위한 적절한 노드 집합을 찾는 혼합 정수 2차 제약 프로그래밍 공식을 해결하여 전역 그래프의 하위 집합을 선택합니다. [18] 궤적 계획 문제를 B-스플라인 표현을 사용하여 혼합 정수 2차 계획법(MIQP)으로 공식화합니다. [19] nan [20] nan [21] nan [22] nan [23] nan [24] 이진 활성화 변수를 도입하여 혼합 정수 2차 프로그램을 생성하는 재구성 방법이 제시됩니다. [25] nan [26] 균일한 전송 전력을 위한 혼합 정수 선형 계획법(MILP) 공식과 전력 할당을 위한 혼합 정수 2차 제약 계획법(MIQCP)으로 문제를 던지고 두 방법을 비교합니다. [27] 제안하는 혼합 정수 2차 모델은 고객의 소비 패턴, 품목 용량과 관련된 연관 규칙으로 구성되며 품목 간 거리와 스테이징 영역을 모두 저장합니다. [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] nan [38] nan [39] nan [40] nan [41] nan [42] nan [43] nan [44] nan [45] nan [46] nan [47] nan [48] nan [49] nan [50]
Mix Integer Quadratic
The optimization of the ESS operation is solved using mix integer quadratic programming (MIQP). [1] The bi-level model is reformulated as a single -level linear problem by Karush Kuhn Turck (KKT) conditions and recast to a mix integer quadratic program using strong duality theory. [2]ESS 운영의 최적화는 혼합 정수 2차 계획법(MIQP)을 사용하여 해결됩니다. [1] 이중 수준 모델은 Karush Kuhn Turck(KKT) 조건에 의해 단일 수준 선형 문제로 재구성되고 강력한 이중성 이론을 사용하여 혼합 정수 2차 프로그램으로 재구성됩니다. [2]
Binary Integer Quadratic
Together with the power flow model, the system power loss and system voltage unbalance indices are formulated as a binary integer quadratic model. [1] They have proposed a mixed binary integer quadratic programming (MIQP) model for cross-functional team selection. [2]전력 흐름 모델과 함께 시스템 전력 손실 및 시스템 전압 불균형 지수는 이진 정수 2차 모델로 공식화됩니다. [1] 그들은 교차 기능 팀 선택을 위한 혼합 이진 정수 2차 계획법(MIQP) 모델을 제안했습니다. [2]
integer quadratic programming 정수 2차 계획법
We then formulate the vRAN energy consumption optimization as an integer quadratic programming problem, whose NP-hard nature leads us to develop GreenRAN, a novel, computationally efficient and distributed solution that leverages Lagrangian decomposition and simulated annealing. [1] Third, a mixed-integer quadratic programming (MIQP) formulation based on the least-squares regression builds optimizable surrogate functions for the variables of interest from the blending operations. [2] The proposed method solves a comprehensive mixed-integer quadratic programming (MIQP) problem to minimizes the charging and battery degradation cost for electric vehicle (EV) fleets considering individual charging and discharging constraints and asymmetric price profiles. [3] Firstly, a mixed integer quadratic programming (MIQP) model is formulated to describe unit commitment problems (UCPs) for each microgrid (MG) and energy transactions among MMGs. [4] By encoding SwarmSTL formulas as mixed binary-integer constraints on the swarm features, the motion planning problem is formulated as a mixed-integer quadratic programming (MIQP) problem. [5] The core of AMPS-Inf relies on the formulation and solution of a Mixed-Integer Quadratic Programming problem for model partitioning and resource provisioning with the objective of minimizing cost without violating response time SLO. [6] The lower layer is the path tracking controller based on hybrid MPC (hMPC), and the coordinated control inputs (yaw moment and the front wheel steering angle) are solved by a Mixed-Integer Quadratic Programming (MIQP) with the piecewise affine (PWA) tire model considering tire saturation region. [7] Secondly, the nonlinear terms in the original optimization equation are eliminated, and it is converted into a mixed integer quadratic programming(MIQP) problem so that it can be solved using the standard method. [8] The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. [9] Then, based on the duality theory for multi-objective optimization, we transform the min–max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. [10] By using the mixed integer quadratic programming (MIQP), a joint spatial-temporal resource allocation scheme is obtained. [11] Indeed, the BM distribution problem can be formulated as minimization of the total potential energy from all treatment sessions subject to delivery time constraints in mixed-integer quadratic programming (MIQP). [12] The latter is in charge to dictate the optimal aggregated signals to every aggregator by employing a mixed-integer quadratic programming (MIQP) approach. [13] Then, the proposed bilevel model is transformed into a mixed integer quadratic programming model using duality theory and Karush-Kuhn-Tucker conditions. [14] As the proposed control algorithm presents both continuous and binary variables, its associated optimization problem is formulated using the Mixed Logic Dynamic (MLD) framework, which results in a Mixed Integer Quadratic Programming problem (MIQP). [15] Conditional Value at Risk (CVaR) based mixed integer quadratic programming formulation is built to hedge the risks of random charging demands and volatile market prices. [16] That is to say, to tackle the intractability, we reformulate the robust counterpart into a conservative approximation which is a mixed-integer quadratic programming, and then effective solutions can be produced with Benders Decomposition algorithm. [17] Using linearization techniques, this model converted a classic mixed-integer quadratic programming (MIQP) problem and solved it efficiently. [18] ESSs sizing optimization and power system scheduling optimization are simultaneously conducted and it is converted to a mixed-integer quadratic programming (MIQP) model with special modeling techniques. [19] Given the different characteristics of the operating variables and planning variables, the optimization target is solved by the method of combining improved particle swarm algorithm using the Shannon information entropy model and mixed-integer quadratic programming (MIQP) based on the ideas of hierarchical decoupling. [20] The energy management problem of the hybrid microgrid is formulated as a mixed-integer quadratic programming (MIQP) model, considering DER and energy storage system operation constraints, system operation constraints, and converter operation constraints. [21] The economic dispatch of a CCHP microgrid with AA-CAES is then modeled as mixed integer quadratic programming (MIQP). [22] In this paper, a mixed integer quadratic programming (MIQCP) based post-event distribution restoration framework in response to high impact low probable events is propounded to maintain the power supply continuity of critical loads particularly in emergency conditions. [23] To attack this challenge, this paper proposes the information masking (IM) method for the energy management problem in the form of mixed-integer quadratic programming (MIQP). [24] The techno-economic feasibility of implementing phase change materials, which includes eutectic salt, polyethylene glycol and paraffin as cold thermal energy storage media for district cooling, have been evaluated by computing the economic dispatch of a cooling bus network using mixed-integer quadratic programming. [25] An integer quadratic programming model is first proposed to formulate this problem. [26] The optimization problem is also modeled as a mixed-integer quadratic programming (MIQCP) problem, solved using the CPLEX solver in GAMS software. [27] Firstly, the centroid change curve is drawn based on greedy strategy and mixed integer quadratic programming in this study, then the strategic optimization calculation model that can accurately describe the oil supply problem is established. [28] The mixed integer quadratic programming (MIQP) is used for sub-problem modeling. [29] This problem which is known as the workload smoothing line balancing problem (WSLBP) is a mixed-integer quadratic programming problem. [30] We distill the design choices made by animators into mathematical objectives that we optimize as the solution to an integer quadratic programming problem. [31] Firstly, a suitable mixed-integer quadratic programming (MIQP) model of the corresponding UC problem is presented by some linearization techniques, which is difficult to solve directly. [32] After the cumulative integration of the results, a series of mathematical programming models are applied in the second phase, that of multicriteria portfolio optimization; a mixed-integer quadratic programming model, a goal programming model, a genetic algorithm model, and a multiobjective PROMETHEE flow model. [33] To solve the raised model, sequence operation theory is introduced to convert the chance constraint into its deterministic equivalent form, and thereby, the leader-follower Stackelberg game is tackled into a mixed-integer quadratic programming formulation through Karush-Kuhn-Tucker optimality conditions and is finally solved by the CPLEX optimizer. [34] The model is a mixed integer quadratic programming problem, which is solved by Gurobi. [35] Subsequently, the proposed modified DistFlow model is applied to the problem of reactive power optimization and network reconfiguration, transforming it into a mixed-integer quadratic programming (MIQP). [36] In the first stage, a mixed-integer quadratic programming model is formulated to obtain an initial radial network topology. [37] I address cases where a top-class commercial mixed-integer quadratic programming solver fails to provide efficient portfolios attempted to be derived by Chebyshev scalarization of the bi-objective optimization problem within a given time limit. [38] The model is formulated as a multistage stochastic mixed-integer quadratic programming problem combining risk-sensitive cost and scenario analysis approaches. [39] We formulate the trajectory planning problem as Mixed Integer Quadratic Programming (MIQP) with a B-spline representation. [40] Thus, the mixed logical dynamical framework is further used to model the intelligent vehicle longitudinal dynamics, and a HMPC controller, which allows us to optimize the switching sequences of the operation modes (binary control inputs) and the torques acted on the wheels (continuous control inputs), is tuned based on online mixed-integer quadratic programming. [41] Then the path planning optimization problem is formulated by mixed-integer quadratic programming (MIQP) to calculate trajectories collision-free with obstacles. [42] The UC and DR problems are solved using Mixed-Integer Quadratic Programming (MIQP). [43] The energy trading of IMOs is formulated as a mixed integer quadratic programming (MIQP) in the outer level. [44] Then, to reduce the difficulty of calculation, the original problem is simplified into a series of nested mixed integer quadratic programming problems by using forward selection strategy. [45] The optimization of the ESS operation is solved using mix integer quadratic programming (MIQP). [46] We pose the active sample selection as an NP-hard integer quadratic programming problem and exploit the Iterative Truncated Power algorithm to derive an efficient solution. [47] First, the optimal allocation and scheduling problem is proposed as a mixed-integer quadratic programming (MIQP) problem. [48] Our previous work [1] relied on mixed-integer quadratic programming (MIQP) for planning, while controllable sets of hybrid automata were used to assess feasibility. [49] Specifically, we develop a mixed integer quadratic programming to determine the optimal size and schedules of a grid-scale ESS in an Australian distribution network. [50]그런 다음 vRAN 에너지 소비 최적화를 정수 2차 계획법 문제로 공식화합니다. 이 문제의 NP-hard 특성은 Lagrangian 분해 및 시뮬레이션된 어닐링을 활용하는 새롭고 계산 효율적인 분산 솔루션인 GreenRAN을 개발하도록 이끕니다. [1] 셋째, 최소 제곱 회귀를 기반으로 하는 혼합 정수 2차 계획법(MIQP) 공식은 혼합 작업에서 관심 변수에 대해 최적화 가능한 대리 함수를 빌드합니다. [2] 제안된 방법은 개별 충전 및 방전 제약과 비대칭 가격 프로필을 고려하여 전기 자동차(EV) 차량의 충전 및 배터리 성능 저하 비용을 최소화하기 위해 포괄적인 혼합 정수 2차 계획법(MIQP) 문제를 해결합니다. [3] 첫째, 혼합 정수 2차 계획법(MIQP) 모델은 각 마이크로그리드(MG)에 대한 단위 확약 문제(UCP)와 MMG 간의 에너지 거래를 설명하기 위해 공식화됩니다. [4] SwarmSTL 공식을 떼 기능에 대한 혼합 이진 정수 제약 조건으로 인코딩함으로써 모션 계획 문제는 혼합 정수 2차 계획법(MIQP) 문제로 공식화됩니다. [5] AMPS-Inf의 핵심은 응답 시간 SLO를 위반하지 않고 비용을 최소화한다는 목표로 모델 분할 및 리소스 프로비저닝을 위한 혼합 정수 2차 계획법 문제의 공식화 및 솔루션에 의존합니다. [6] 하위 계층은 하이브리드 MPC(hMPC)를 기반으로 하는 경로 추적 컨트롤러이며 조정된 제어 입력(요 모멘트 및 앞바퀴 조향 각도)은 PWA(조각별 아핀)가 있는 MIQP(혼합 정수 2차 계획법)에 의해 해결됩니다. 타이어 포화 영역을 고려한 타이어 모델. [7] nan [8] 제어 문제는 논리, 정수, 혼합 및 확률 변수의 존재로 인해 확률적 모델 예측 제어(SMPC) 기술 및 혼합 정수 2차 계획법(MIQP)을 사용하여 개발됩니다. [9] 그런 다음 다중 목표 최적화를 위한 이중성 이론을 기반으로 최소-최대 문제를 혼합 정수 2차 계획법 문제로 변환하여 스케줄링 문제의 동등하고 강력한 대응물을 효과적으로 해결합니다. [10] nan [11] 실제로, BM 분포 문제는 혼합 정수 2차 계획법(MIQP)의 전달 시간 제약을 받는 모든 치료 세션에서 총 포텐셜 에너지의 최소화로 공식화될 수 있습니다. [12] 후자는 혼합 정수 2차 계획법(MIQP) 접근 방식을 사용하여 모든 집계기에 최적의 집계 신호를 지시하는 역할을 합니다. [13] nan [14] nan [15] nan [16] 즉, 다루기 힘든 문제를 해결하기 위해 강력한 상대를 혼합 정수 2차 계획법인 보수적 근사로 재구성한 다음 Benders Decomposition 알고리즘을 사용하여 효과적인 솔루션을 생성할 수 있습니다. [17] 선형화 기술을 사용하여 이 모델은 고전적인 혼합 정수 2차 계획법(MIQP) 문제를 변환하고 효율적으로 해결했습니다. [18] ESS의 크기 최적화와 전력계통 스케줄링 최적화를 동시에 수행하여 특수 모델링 기법을 사용하여 혼합 정수 2차 계획법(MIQP) 모델로 변환합니다. [19] 운영 변수와 계획 변수의 다른 특성을 감안할 때 Shannon 정보 엔트로피 모델을 사용하는 개선된 입자 군집 알고리즘과 계층적 디커플링 아이디어를 기반으로 하는 혼합 정수 2차 계획법(MIQP)을 결합하는 방법으로 최적화 목표를 해결합니다. [20] 하이브리드 마이크로그리드의 에너지 관리 문제는 DER 및 에너지 저장 시스템 운영 제약, 시스템 운영 제약, 컨버터 운영 제약을 고려하여 혼합 정수 2차 계획법(MIQP) 모델로 공식화됩니다. [21] nan [22] nan [23] 이 문제를 해결하기 위해 본 논문에서는 에너지 관리 문제에 대한 정보 마스킹(IM) 방법을 MIQP(Mixed Integer Quadratic Programming) 형식으로 제안합니다. [24] 지역 냉방을 위한 저온 열에너지 저장 매체로 공융염, 폴리에틸렌 글리콜 및 파라핀을 포함하는 상변화 물질을 구현하는 기술 경제적 타당성은 혼합 정수 2차 계획법을 사용하여 냉각 버스 네트워크의 경제적 디스패치를 계산하여 평가되었습니다. [25] nan [26] 최적화 문제는 또한 GAMS 소프트웨어의 CPLEX 솔버를 사용하여 해결되는 혼합 정수 2차 계획법(MIQCP) 문제로 모델링됩니다. [27] nan [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] nan [38] 이 모델은 위험에 민감한 비용 및 시나리오 분석 접근 방식을 결합한 다단계 확률적 혼합 정수 2차 계획법 문제로 공식화됩니다. [39] 궤적 계획 문제를 B-스플라인 표현을 사용하여 혼합 정수 2차 계획법(MIQP)으로 공식화합니다. [40] nan [41] nan [42] nan [43] nan [44] nan [45] ESS 운영의 최적화는 혼합 정수 2차 계획법(MIQP)을 사용하여 해결됩니다. [46] nan [47] nan [48] nan [49] nan [50]
integer quadratic program 정수 2차 계획법
The second method uses a hierarchical scheme with two main units: a trajectory-planning layer based on mixed-integer quadratic program (MIQP) computes an on-line collision-free trajectory using simplified motion dynamics, and a tracking controller unit to follow the trajectory from the higher level using the nonlinear vehicle model. [1] We consider the global optimization of nonconvex quadratic programs and mixed-integer quadratic programs. [2] The models of the decentralized D-GSS scheme are built with the bulk system GSS modeled as a mixed-integer quadratic program (MIQP), the DS multi-step operation modeled as mixed-integer second-order conic programs (MISOCPs) and reliable power supply assessments of renewable energy sources as linear programs (LPs). [3] In this case, we provide a practical approach to obtain such an approximation defined by the smallest integer coefficients possible, which requires solving a few, small-size integer quadratic programs. [4] We model the problem as a mixed integer quadratic program and linearize it. [5] Based on the quasi-steady-state assumption of thermal balance, the model is simplified to a mixed-integer quadratic program problem, and it is solved by Gurobi. [6] To this end, we reformulate the channel selection problem as a mixed-integer quadratic program (MIQP), which allows the use of efficient MIQP solvers to find the optimal channel combination in a feasible computation time for up to 100 candidate channels. [7] We also present some encouraging computational results, obtained by applying our inequalities to (a) integer quadratic programs with box constraints and (b) portfolio optimisation problems with semi-continuous variables. [8] In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. [9] The problem is solved offline as a mixed integer quadratic program, which generates trajectories for the velocity and for freewheeling. [10] The verifier then solves a mixed-integer quadratic program in the state space to either validate the proposed Lyapunov function candidate or reject it with a counterexample, i. [11] The problem is formulated as a mixed-integer quadratic program where the integer constraints ensure collision avoidance. [12] The method leverages a new Mixed-Integer Quadratic Program/Model Predictive Control formulation that allows to easily account for drag forces. [13] Referring to the variance criterion, we first propose a reformulation of the problem as an Integer Quadratic Program that, however, does not have particular structural properties that may help in finding good lower bounds on the optimal value of the problem. [14] We first present a mixed-integer quadratic program (MIQP) formulation of the problem. [15] In hybrid model predictive control (MPC), a mixed-integer quadratic program (MIQP) is solved at each sampling time to compute the optimal control action. [16] This paper develops a method to learn the optimal strategy from a mixed-integer quadratic program with time-varying parameters, which can model many power system operation problems such as unit commitment and optimal power flow. [17] We consider the global optimization of nonconvex mixed-integer quadratic programs with linear equality constraints. [18] The COVID-19 pandemic has had an enormous impact on the public transport sector After the start of the pandemic, passenger demand dropped significantly for public transport services In addition, social distancing measures have resulted in introducing pandemic-imposed capacity limitations to public transport vehicles Consequently, public transport operators should adjust their planning to minimize the impact of the COVID-19 pandemic This study introduces a mixed-integer quadratic program that sets the optimal frequencies of public transport lines and sublines in order to conform with the pandemic-imposed capacity The focus is on cases where the public transport demand is high, but the crowding levels inside public transport vehicles should remain below the pandemic-imposed capacities Of particular interest are public transport lines with skewed demand profiles that can benefit from the introduction of short-turning sublines that serve the high-demand line segments The frequency setting model is tested on a network containing two high-demand bus lines in the Twente region in the Netherlands, and it demonstrates that the revenue losses due to social distancing can be reduced when implementing short-turning service patterns. [19] The verifier then solves a nonconvex mixed-integer quadratic program in the state space to either validate the proposed Lyapunov function candidate or reject it with a counterexample, i. [20] Most of the existing methods for the problem are based on first converting the OTS into a mixed-integer linear program (MILP) or mixed-integer quadratic program (MIQP), and then iteratively solving a series of its convex relaxations. [21] A reformulation method is presented which results in a mixed integer quadratic program by introducing binary activation variables. [22] The bi-level model is reformulated as a single -level linear problem by Karush Kuhn Turck (KKT) conditions and recast to a mix integer quadratic program using strong duality theory. [23] Mixed-integer model predictive control (MI-MPC) requires the solution of a mixed-integer quadratic program (MIQP) at each sampling instant under strict timing constraints, where part of the state and control variables can only assume a discrete set of values. [24] A mixed-integer quadratic program is formulated for the design of the predictive controller constrained to the dynamic model and implementing a pulse width modulation. [25] A finite-control-set MPC problem is formulated as a mixed-integer quadratic program subject to the dynamic LC model and pulse width modulators. [26] We used undirected graphs to obtain a representation of the system and subsequently solved the resulting NP-hard problem as a mixed-integer quadratic program (MIQP). [27] Furthermore, we present a Mixed Integer Quadratic Program formulation in which the solver can choose the trajectory interval allocation, and where a time allocation heuristic is computed efficiently using the result of the previous replanning iteration. [28] The proposed method reformulates the optimization problem as a mixed-integer quadratic program (MIQP), allowing then to obtain the global optimal solution by using an off-the-shelf optimization software. [29] The first method considered in this study is a mixed integer quadratic program MPC based on mixed linear dynamic formulations. [30] This problem is reformulated to a mixed-integer quadratic program (MIQP). [31] The formulated optimization problem can be classified as a convex Mixed Integer Quadratic Program; consequently, its solution can be found using readily available generic solvers. [32] We show that MINIGL-ED is fixed-parameter tractable for parameter T and vertex cover by modeling the problem as an integer quadratic program. [33] The online optimization in MPC is formulated as a Mixed Integer Quadratic Program (MIQP), and can be achieved using a branch and bound algorithm where multiple relaxed Quadratic Programs (QPs) are solved. [34] In hybrid Model Predictive Control (MPC), a Mixed-Integer Quadratic Program (MIQP) is solved at each sampling time to compute the optimal control action. [35] For networks with non-overlapping cycles, a provably exact convex relaxation of the pressure drop equations yields a mixed-integer quadratic program (MIQP)-based WF solver. [36] Although finding the globally optimal trail assignment and transition paths can be formulated as a Mixed Integer Quadratic Program (MIQP), the MIQP is intractable even for small problems. [37] The proposed model is a mixed integer quadratic program. [38] The optimal control problem is solved offline as a Mixed Integer Quadratic Program, which yields reference trajectories that are tracked online in the vehicle. [39] The problem is formulated as a mixed integer quadratic program in a way that could be linearized without significantly increasing the number of variables. [40]두 번째 방법은 두 가지 기본 단위가 있는 계층 구조를 사용합니다. MIQP(혼합 정수 2차 프로그램)를 기반으로 하는 궤적 계획 레이어는 단순화된 모션 역학을 사용하여 온라인 충돌 없는 궤적을 계산하고 궤적을 따라가는 추적 컨트롤러 유닛 비선형 차량 모델을 사용하여 더 높은 수준에서. [1] 비볼록 2차 계획법과 혼합 정수 2차 계획법의 전역 최적화를 고려합니다. [2] 분산형 D-GSS 체계의 모델은 혼합 정수 2차 계획법(MIQP)으로 모델링된 벌크 시스템 GSS, 혼합 정수 2차 원추 계획법(MISOCP)으로 모델링된 DS 다단계 연산 및 안정적인 전력으로 구축됩니다. 선형 프로그램(LP)으로 재생 가능한 에너지원의 공급 평가. [3] nan [4] nan [5] 열평형의 준정상상태 가정에 기초하여 모델을 혼합 정수 2차 프로그램 문제로 단순화하고 구로비에 의해 푼다. [6] 이를 위해 효율적인 MIQP 솔버를 사용하여 최대 100개의 후보 채널에 대해 실행 가능한 계산 시간에 최적의 채널 조합을 찾을 수 있도록 하는 혼합 정수 2차 프로그램(MIQP)으로 채널 선택 문제를 다시 공식화합니다. [7] 우리는 또한 (a) 상자 제약 조건이 있는 정수 2차 프로그램 및 (b) 반연속 변수가 있는 포트폴리오 최적화 문제에 부등식을 적용하여 얻은 몇 가지 고무적인 계산 결과를 제시합니다. [8] 이 목표를 향한 움직임에서 혼합 정수 2차 프로그램으로 캐스트된 후진 수평선 제어는 결합 최적화에서 차선 변경 및 가속을 계획하는 데 사용됩니다. [9] nan [10] 그런 다음 검증자는 상태 공간에서 혼합 정수 2차 프로그램을 해결하여 제안된 리아푸노프 함수 후보를 검증하거나 반대 예, 즉 i를 사용하여 거부합니다. [11] 문제는 정수 제약 조건이 충돌 회피를 보장하는 혼합 정수 2차 프로그램으로 공식화됩니다. [12] 이 방법은 항력을 쉽게 설명할 수 있는 새로운 혼합 정수 2차 프로그램/모델 예측 제어 공식을 활용합니다. [13] 분산 기준을 참조하여, 우리는 먼저 문제의 최적 값에 대한 좋은 하한을 찾는 데 도움이 될 수 있는 특정 구조적 속성을 갖지 않는 정수 2차 프로그램으로 문제를 재구성할 것을 제안합니다. [14] 먼저 문제의 혼합 정수 2차 계획법(MIQP) 공식을 제시합니다. [15] 하이브리드 모델 예측 제어(MPC)에서 혼합 정수 2차 계획법(MIQP)은 최적의 제어 동작을 계산하기 위해 각 샘플링 시간에 해결됩니다. [16] nan [17] nan [18] nan [19] nan [20] 문제에 대한 기존 방법의 대부분은 먼저 OTS를 혼합 정수 선형 계획법(MILP) 또는 혼합 정수 2차 계획법(MIQP)으로 변환한 다음 일련의 볼록 완화를 반복적으로 푸는 방식을 기반으로 합니다. [21] 이진 활성화 변수를 도입하여 혼합 정수 2차 프로그램을 생성하는 재구성 방법이 제시됩니다. [22] 이중 수준 모델은 Karush Kuhn Turck(KKT) 조건에 의해 단일 수준 선형 문제로 재구성되고 강력한 이중성 이론을 사용하여 혼합 정수 2차 프로그램으로 재구성됩니다. [23] nan [24] nan [25] nan [26] nan [27] nan [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] nan [38] nan [39] nan [40]
integer quadratic optimization 정수 2차 최적화
In this paper we show how to improve existing mixedinteger quadratic optimization formulations for this problem. [1] For this problem, we derive a mixed-integer quadratic optimization (MIQO) formulation by applying a piecewise-linear approximation to the log-likelihood function. [2] In this paper we present the Julia package miqoGraph, which uses mixed-integer quadratic optimization to fit topology, drift lengths, and admixture proportions simultaneously. [3] We use the penalized negative log-likelihood score function with both $\ell_0$ and $\ell_1$ regularizations and propose a new mixed-integer quadratic optimization (MIQO) model, referred to as a layered network (LN) formulation. [4] In order to solve mixed-integer quadratic optimization problem, we suggested nonlinear neural network based on coefficient of variation for solving cardinality constraint portfolio optimization (CCPO) problem. [5] In this paper, we propose mixed integer quadratic optimization (MIQO) formulations for selecting the best subset of explanatory variables subject to the upper bounds on the VIFs of selected variables. [6] In this paper we present the Julia package miqoGraph, which uses mixed-integer quadratic optimization to fit topology, drift lengths, and admixture proportions simultaneously. [7] We formulate a finite-horizon mixed-integer quadratic optimization problem with multiple chance constraints at each sampling time, which is in general a non-convex problem and difficult to solve. [8] In this work, a bi-level mixed-integer quadratic optimization problem is proposed for the decentralized selection of a portfolio of financial securities and real investments. [9] A novel algorithm for the global solution of a class of tri-level mixed-integer quadratic optimization problems containing both integer and continuous variables at all three optimization levels is presented. [10]이 논문에서 우리는 이 문제에 대한 기존의 혼합 정수 2차 최적화 공식을 개선하는 방법을 보여줍니다. [1] 이 문제를 위해 우리는 로그 우도 함수에 조각별 선형 근사를 적용하여 혼합 정수 2차 최적화(MIQO) 공식을 유도합니다. [2] nan [3] 우리는 $\ell_0$ 및 $\ell_1$ 정규화와 함께 벌점을 받는 음수 로그 가능성 점수 함수를 사용하고 계층 네트워크(LN) 공식이라고 하는 새로운 혼합 정수 2차 최적화(MIQO) 모델을 제안합니다. [4] 혼합 정수 2차 최적화 문제를 해결하기 위해 카디널리티 제약 포트폴리오 최적화(CCPO) 문제를 해결하기 위해 변동 계수를 기반으로 하는 비선형 신경망을 제안했습니다. [5] nan [6] nan [7] nan [8] nan [9] nan [10]
integer quadratic problem 정수 2차 문제
The optimization problems are formulated as mixed-integer quadratic problems, using a Gaussian copula methodology to generate PV scenarios, to approximate the mixed-integer non-linear problem of the capacity firming. [1] Consequently, the overall setup leads to a nonconvex optimization problem but is then shown to be transformed into a mixed-integer quadratic problem that can be implemented efficiently. [2] We provide several reformulations to obtain a single-level mixed-integer quadratic problem. [3] The optimization problem is formulated as a Mixed Integer Quadratic Problem and uses the different power references as levers to reach this optimum and maintain the state of the microgrid within limitations. [4]최적화 문제는 용량 확정의 혼합 정수 비선형 문제를 근사하기 위해 PV 시나리오를 생성하기 위해 가우스 코퓰러 방법론을 사용하여 혼합 정수 2차 문제로 공식화됩니다. [1] 결과적으로 전체 설정은 비볼록 최적화 문제로 이어지지만 효율적으로 구현할 수 있는 혼합 정수 2차 문제로 변환되는 것으로 나타납니다. [2] nan [3] nan [4]
integer quadratic constraint 정수 2차 제약 조건
The proposed load shedding optimization and restoration optimization are linearized to mixed-integer quadratic constraint programming (MIQCP) models. [1] The second-order cone relaxation method and the incremental formulation for the piecewise linearization method are utilized to transform the proposed nonlinear model into mixed integer quadratic constraint programming model, which can be effectively solved by the commercial solver. [2] We cast our problem as mixed integer linear programming (MILP) formulation for uniform transmit power and mixed integer quadratic constraint programming (MIQCP) for power allocation and compare between both methods. [3]제안된 부하 분산 최적화 및 복원 최적화는 혼합 정수 2차 제약 프로그래밍(MIQCP) 모델로 선형화됩니다. [1] 제안된 비선형 모델을 상용 솔버로 효과적으로 해결할 수 있는 혼합 정수 2차 제약 조건 프로그래밍 모델로 변환하기 위해 2차 원뿔 이완 방법과 조각별 선형화 방법에 대한 증분 공식이 활용됩니다. [2] 균일한 전송 전력을 위한 혼합 정수 선형 계획법(MILP) 공식과 전력 할당을 위한 혼합 정수 2차 제약 계획법(MIQCP)으로 문제를 던지고 두 방법을 비교합니다. [3]
integer quadratic model
The proposed mixed integer quadratic model consists of associations rules related to a customer’s consumption pattern, capacity of the items, and stores both the distance among items and staging area. [1] Together with the power flow model, the system power loss and system voltage unbalance indices are formulated as a binary integer quadratic model. [2] In addition, we formulated the studied problem as a mixed integer quadratic model that is sued to generate optimal solutions. [3]제안하는 혼합 정수 2차 모델은 고객의 소비 패턴, 품목 용량과 관련된 연관 규칙으로 구성되며 품목 간 거리와 스테이징 영역을 모두 저장합니다. [1] 전력 흐름 모델과 함께 시스템 전력 손실 및 시스템 전압 불균형 지수는 이진 정수 2차 모델로 공식화됩니다. [2] nan [3]
integer quadratic convex
With this study, we address the optimal phase balancing problem in three-phase networks with asymmetric loads in reference to a mixed-integer quadratic convex (MIQC) model. [1] To this end, we specialize the Mixed-Integer Quadratic Convex Reformulation method (MIQCR) to (OPF). [2]이 연구를 통해 MIQC(Mixed Integer Quadratic convex) 모델을 참조하여 비대칭 부하가 있는 3상 네트워크에서 최적의 위상 균형 문제를 해결합니다. [1] nan [2]
integer quadratic constrained
This model is then converted into a mixed integer quadratic constrained problem that can be solved with the CPLEX solver. [1] Additionally, we compare a Mixed Integer Linear Programming (MILP) approach to Mixed Integer Quadratic Constrained Programming (MIQCP). [2]그런 다음 이 모델은 CPLEX 솔버로 해결할 수 있는 혼합 정수 2차 제약 조건 문제로 변환됩니다. [1] 또한 혼합 정수 선형 계획법(MILP) 접근 방식을 혼합 정수 2차 제약 계획법(MIQCP)과 비교합니다. [2]
integer quadratic integrate 정수 2차 적분
To address this challenge, we design a neuron model: the Integer Quadratic Integrate-and-Fire (IQIF) neuron. [1] Also, these neurons in the processor can be configured as novel models, integer quadratic integrate-and-fire neuron models, which contain an unsigned 8-bit membrane potential value. [2]이 문제를 해결하기 위해 우리는 뉴런 모델인 IQIF(Integer Quadratic Integrate-and-Fire) 뉴런을 설계합니다. [1] 또한 프로세서의 이러한 뉴런은 부호 없는 8비트 멤브레인 전위 값을 포함하는 새로운 모델인 정수 2차 적분 및 화재 뉴런 모델로 구성할 수 있습니다. [2]