ライフサイエンスコーパス: linear programming

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

2 l restraints by machine learning and integer linear programming.

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

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

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

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

7 flux distributions at steady state by using linear programming.

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

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

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

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

12 Linear programming and uniform random sampling were appl

13 0% of the time required by the mixed-integer linear programming approach available in the literature.

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

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

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

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

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

19 Linear programming calculated optimal flux distributions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

69 Linear programming was used iteratively to produce a die

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

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

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