ライフサイエンスコーパス: linear programming

1 thways and optimal flux distributions (using linear programming).

2 equently infers potential PTM patterns using linear programming.

3 uristic and an exact algorithm using integer linear programming.

4 rate codon design (DeCoDe), based on integer linear programming.

5 l restraints by machine learning and integer linear programming.

6 putes the exact (r)SPR distance with integer linear programming.

7 tions, the sparsest solution can be found by linear programming.

8 n is sufficiently sparse, it can be found by linear programming.

9 eros are uniquely recoverable from y = Ax by linear programming.

10 flux distributions at steady state by using linear programming.

11 We then coupled integer linear programming, a method to optimize protein fitness

12 imization algorithm termed alternate integer linear programming (AILP).

13 the VSTS were modeled using a Mixed Integer Linear Programming algorithm across 156 simulations.

14 imensions using the Torquato-Jiao sequential linear programming algorithm.

15 nces and the ready availability of efficient linear programming algorithms.

16 Leveraging integer linear programming, ALLEGRO identified compact sgRNA set

17 of atoms in the reactants and products) and Linear Programming analysis (which detects stoichiometri

18 solve it efficiently by using mixed integer linear programming and graph decomposition.

19 Our method is based on Linear Programming and is capable of automatically resol

20 Using linear programming and machine learning, we show that th

21 The framework employs mixed-integer linear programming and nonlinear simulations with large-

22 ariety of quantum techniques such as quantum linear programming and the quantum linear systems algori

23 Linear programming and uniform random sampling were appl

24 t is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negativ

25 0% of the time required by the mixed-integer linear programming approach available in the literature.

26 We devise an integer linear programming approach that solves our problem exac

27 ay; ii) computationally developed an Integer Linear Programming approach to infer OC-mediated myeloma

28 We use a linear programming approach to optimally shift the stead

29 We developed a linear programming based support vector machine with L(1

30 Our approach combines an integer linear programming-based Ising formulation with hardware

31 With the advance of fast linear programming-based network reconciliation, the eff

32 vercome the problem, this article proposes a linear programming-based topology determination (LPTD) m

33 We present a novel algorithm that uses a linear programming-based tree search and efficiently enu

34 Linear programming calculated optimal flux distributions

35 problem (of the so-called mixed-integer non-linear programming class) and we developed an algorithmi

36 because our approach cannot be formulated as linear programming, (d) the use of Binary Decision Diagr

37 balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of

38 We give a decision procedure based on linear programming for deciding, for certain real-valued

39 The method uses integer linear programming for finding optimal alignments, embed

40 Our approach combines a novel linear programming formulation for interface alignment w

41 We propose an Integer Linear Programming formulation for it, and implement it

42 use the ECV criterion to develop an integer linear programming formulation for the parental selectio

43 whole genome sequence data, using an integer linear programming formulation, as an update to the Immu

44 ven limit, improving on the existing integer-linear programming formulation.

45 riven by a globally convergent mixed-integer linear programming formulation.

46 y integrating such information in an integer linear programming framework, we demonstrate on simulate

47 Many mathematical models, including linear programming, have been adopted to tackle this iss

48 datasets, including (i) an efficient integer linear programming, (ii) a probabilistic logic implement

49 We present an integer linear programming (ILP) formulation of side-chain posit

50 y test" for paths based on a general integer linear programming (ILP) formulation.

51 is study, we propose polynomial-size integer linear programming (ILP) formulations for the aforementi

52 low with the set cover problem in an integer linear programming (ILP) framework to simultaneously ide

53 these gaps, this study proposes two integer linear programming (ILP) models to optimize sensor place

54 We present a new Integer Linear Programming (ILP) solution for maximum likelihood

55 ormulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally

56 Subsequently, an algorithm based on linear programming is developed as a decision-making str

57 Optifood, developed by the WHO and based on linear programming (LP) analysis, has been developed to

58 h asymptotic and empirical improvements over linear programming (LP) approaches to the problem.

59 grality constraint to give a polynomial-time linear programming (LP) heuristic.

60 This study uses results from Linear Programming (LP) modeling data to examine the pot

61 Using linear programming (LP) modeling of the various refinery

62 be rapidly converged by iteratively solving linear programming (LP) problem and the number of iterat

63 imulation of phototrophic growth as a single linear programming (LP) problem.

64 solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential m

65 ar growth) and the subsequent application of linear programming (LP) to identify fluxes through a rea

66 because it requires repeated application of linear programming (LP) to obtain flux balance analysis

67 The objective was to develop a comprehensive linear programming (LP) tool to create novel RUTF formul

68 methods including clustering, binarization, linear programming (LP), Boolean function determination,

69 The current descriptions were compared with linear programming (LP)-based flux descriptions using th

70 ly available foods can be developed by using linear programming (LP).

71 Applying a linear programming method and a parameter sweep algorith

72 rence in metaproteomics using an integrative linear programming method.

73 topologies are estimated using mixed integer linear programming (MILP) and in phase III the optimal t

74 Utilising mixed integer linear programming (MILP) and Q-learning models, this st

75 est scheduling, we propose two mixed-integer linear programming (MILP) models and a heuristic algorit

76 Mixed-Integer Linear Programming (MILP) plays an important role across

77 based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which si

78 minimum set of reactions using Mixed-Integer Linear Programming (MILP).

79 ool, called MetaFlux, based on mixed integer linear programming (MILP).

80 nt an exact algorithm based on mixed integer linear programming (MILP).

81 an optimization tool known as mixed-integer linear programming (MILP).

82 level were optimized using an Integer/Binary Linear Programming Model to maximize watershed-scale fin

83 20 bird species, land values, and an integer linear programming model to prioritize land units (1 km(

84 Using a linear programming model, we ask when a colony should pr

85 orating taxonomic abundance information in a linear programming model.

86 Linear programming modeling of the U.S. refining sector

87 This analysis uses linear programming modeling of the U.S. refining sector

88 A reduction in diet costs in linear programming models leads to high-fat, energy-dens

89 ghts have been optimized by high-performance linear programming models that systematically find the o

90 We reformulate and generalize two binary linear programming models that tackle this challenge, de

91 The proposed Non Linear Programming (NLP) formulation allows for fast opt

92 y completes a reaction network by using only Linear Programming, not MILP.

93 Selective and linear programming of a redox transistor array is execut

94 In addition, a non-linear programming optimal control problem is introduced

95 ical formula calculation using mixed integer linear programming optimization (RAMSI).

96 ar optimization (also referred to as integer linear programming or ILP) and tandem mass spectrometry

97 m in SimCCS is structured as a mixed-integer linear programming problem by selecting the optimal pipe

98 demonstrate how this can be formulated as a linear programming problem, enabling the fast and effici

99 roblem is approximated as a single-objective linear programming problem, which is efficiently solved

100 injections, is formulated as a mixed integer linear programming problem.

101 manipulated algebraically into the form of a linear programming problem.

102 a new methodology for solving Mixed Integer Linear Programming problems with constraints for the sys

103 mRNA expression profiles by solving multiple linear programming problems.

104 the U.S. LDV fleet are estimated based on a linear-programming refinery model, a historically calibr

105 g diet models cannot be solved with standard linear programming software.

106 We derive an integer linear programming solution to the VAF factorization pro

107 are close to the lower bound inferred from a linear programming solution.

108 on of tasks requiring iterative calls to the linear programming solver.

109 sfiability Modulo Theory solvers rather than Linear Programming solvers, because our approach cannot

110 distances, and then leverage modern integer linear programming solvers.

111 Moreover, the same basic linear programming structure is used for both unconstrai

112 ation while retaining a potentially scalable linear programming structure.

113 w the ECV criterion and the proposed integer linear programming techniques can be applied to improve

114 Leveraging mixed-integer linear programming, this framework identifies intricate

115 We develop an optimal method based on linear programming to add noise to individual locations

116 We used techniques based on linear programming to demonstrate the following: (1) tha

117 xed clustering to identify reliable markers, linear programming to detect an initial scatter simplex,

118 The LPmerge software uses linear programming to efficiently minimize the mean abso

119 It re-estimates CNs using integer linear programming to enforce CN balance and then identi

120 polytope have been studied extensively using linear programming to find the optimal flux distribution

121 re is a "phase transition" in the ability of linear programming to find the sparsest nonnegative solu

122 oposed efficient algorithms based on Integer Linear Programming to select a minimum number of non-uni

123 lizes streamlined LCA methods, together with linear programming, to determine optimal portfolios of p

124 Linear programming was used iteratively to produce a die

125 Here mixed-integer linear programming was used to calculate and study a sub

126 Linear programming was used to model the hypothetical ad

127 A combination of flux balance analysis and linear programming was used to simulate cellular metabol

128 el constrained optimization approach (Spline Linear Programming), where the constraints are learned e

129 rithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike

130 h fewer than rhoVS(d/n)d(1 + o(1)) nonzeros, linear programming will find that solution.