A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is `application/pdf`

.

## Filters

##
###
OMLT: Optimization Machine Learning Toolkit
[article]

2022
*
arXiv
*
pre-print

The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems. We discuss the advances in optimization technology that made OMLT possible and show how OMLT seamlessly integrates with the algebraic modeling language Pyomo. We demonstrate how to use OMLT for solving decision-making problems in both computer science and engineering.

arXiv:2202.02414v1
fatcat:b5o2qj3gj5b7nh5qvy5nem4xum
##
###
Scalable Preconditioning of Block-Structured Linear Algebra Systems using ADMM
[article]

2019
*
arXiv
*
pre-print

arXiv:1904.11003v1
fatcat:fis24khdjja3xl5f252ywr6nse
*D*1 J T 1 A T 1 J 1 . . . . . . ...*D*P J T P A T P J P B T 1 · · · B T P A 1 B 1 . . . . . . ...##
###
Generalized Disjunctive Programming
[chapter]

2017
*
Pyomo — Optimization Modeling in Python
*

The set of available diameters (m) is:

doi:10.1007/978-3-319-58821-6_9
fatcat:rpfafu2hijgzldqwlsy7k7klpq
*D*= {.3048; .4064; Figure 1 : 1 a) Two Loop WDN. b) Hanoi WDN. ...*D*' Ambrosio et al. (2014) presented an MINLP model and Spatial Branch and Bound and piecewise linear relaxations were used. ...##
###
Clustering-based preconditioning for stochastic programs

2015
*
Computational optimization and applications
*

The corresponding costs are

doi:10.1007/s10589-015-9813-x
fatcat:wkv5wkb2bnb23mues3xteycbze
*d*g and*d*p . The generation levels in each scenario are given by g s with costs c g . The random demands are*d*s . ... The problem has the following structure: min*d*T g ∆g +*d*T p ∆p + S −1 s∈S c T g g s (39a) s.t. ...##
###
Large-Scale Nonlinear Programming for Multi-scenario Optimization
[chapter]

2008
*
Modeling, Simulation and Optimization of Complex Processes
*

T q = −[(∇ xq L ℓ q ) T , (c ℓ q ) T , (

doi:10.1007/978-3-540-79409-7_22
dblp:conf/hpsc/LairdB06
fatcat:fven6sspvnbpfoaizkhl3tw2he
*D*q x ℓ q −*D*q*d*ℓ ) T ], u T q = [∆x T q ∆λ T q ∆σ T q ], A T q = [ 0 0 −*D*T q ], W q = H ℓ q +δ 1 I ∇ xq c ℓ q*D*T q (∇ xq c ℓ q ) T −δ 2 I 0*D*q 0 −δ 2 I ... q∈Q f q (x q ) s.t . c q (x q ) = 0, S q x q ≥ 0,*D*q x q −*D*q*d*= 0 q ∈ Q (3) where x T q = [z T q y T q s T q*d*T q ] and the*D*q ,*D*q and S q matrices extract suitable components of the x q vector ...##
###
Optimal design of cryogenic air separation columns under uncertainty

2010
*
Computers and Chemical Engineering
*

, & Biegler, 2008; Zhu &

doi:10.1016/j.compchemeng.2010.02.007
fatcat:53n2pngaqjaoxlb3gvo4ywe77a
*Laird*, 2008) . ... Multi-scenario programming approach The multi-scenario formulation can be expressed in general form as, min*d*,u,l P = f 0 (*d*) + k ∈ K q ∈ Q ω qk f qk (*d*, u k , l qk , Â v k , Â u q ) s.t. h qk (*d*, u k ...##
###
Nonlinear programming strategies on high-performance computers

2015
*
2015 54th IEEE Conference on Decision and Control (CDC)
*

Thanks is also extended for partial financial support provided to

doi:10.1109/cdc.2015.7402938
dblp:conf/cdc/KangCLZ15
fatcat:3tx4wnaeargflbzbpvmgubn2ka
*Carl**Laird*by the National Science Foundation (CAREER Grant CBET# 0955205). ... We obtain −*d*T k g k =*d*T k W k (δ w )*d*k − c T k λ + k + 1 δ c (λ + k ) T λ + k , (I.8) where we use the second row of the augmented system to note that J k*d*k − 1 δc λ + k = −c k . ... In a first-order method, we update the duals λ 0 as λ + 0 = λ 0 + ∇ λ0*D*(λ 0 ), (V.31) where ∇ λ0*D*(λ 0 ) = p∈P Π p x p (V.32) is the gradient of*D*(λ 0 ). ...##
###
A fast moving horizon estimation algorithm based on nonlinear programming sensitivity

2008
*
Journal of Process Control
*

The dynamic evolution of these states can be described by material balances around each plant unit,

doi:10.1016/j.jprocont.2008.06.003
fatcat:dadopdhlgfh7hdkczr4mpsv7za
*d*(V k · ρ k · z k,j ) dt = F k z in k,j − F k z k,j z k,j (0) = z k,j 0 k = 1, ..., N U , j = 1, ... ...##
###
Interior-point decomposition approaches for parallel solution of large-scale nonlinear parameter estimation problems

2008
*
Chemical Engineering Science
*

Eliminating Δν k from the resulting linear equation gives the primal-dual augmented system H k Δx k + ∇ x k c k Δλ k +

doi:10.1016/j.ces.2007.05.022
fatcat:om7bndcc6fegreqozkrz3mpot4
*D*T k Δσ k = − ∇ x kL k ∇ x k c k Δx k = −c k*D*k Δx k −*D*k Δ*d*= −*D*k x k +*D*k*d*⎫ ... Writing the Newton step for (12) at iteration leads to: ∇ x k x k L k Δx k + ∇ x k c k Δλ k +*D*T k Δσ k − S T k Δν k = −(∇ x k L k − S T k ν k ) ∇ x k c k Δx k = −c k*D*k Δx k −*D*k Δ*d*= −*D*k x k +*D*...##
###
Fast implementations and rigorous models: Can both be accommodated in NMPC?

2008
*
International Journal of Robust and Nonlinear Control
*

Equation (10) shows the balance for the j-th component for every plant unit:

doi:10.1002/rnc.1250
fatcat:vl52vgx6kjg25j5iaorawr7x7a
*d*V ρw j dt = F w j in − F w j (10) where F is the mass flow rate (kg/h); V , equipment volume (m 3 ); t, time (s); ρ, gas ...##
###
An augmented Lagrangian interior-point approach for large-scale NLP problems on graphics processing units

2016
*
Computers and Chemical Engineering
*

The authors gratefully acknowledge the financial support provided to Yankai Cao and partial financial support provided to

doi:10.1016/j.compchemeng.2015.10.010
fatcat:ewljw6a7kzfmbfn5x2lom4sub4
*Carl**Laird*by the National Science Foundation (CAREER Grant CBET# 0955205). ... After solving equation (10) for*d*x k , the step in the multipliers*d*z k can be obtained using*d*z k = µ in X k −1 e − z k − Σ k*d*x k . (11) After the step directions are determined, the maximum step ... Compute the search direction 5.1 Solve (15) for*d*x k using the PCG method on GPU. . 5.2 Compute*d*z k from (11). . ...##
###
An algorithmic framework for convex mixed integer nonlinear programs

2008
*
Discrete Optimization
*

This paper is motivated by the fact that mixed integer nonlinear programming is an important and difficult area for which there is a need for developing new methods and software for solving large-scale problems. Moreover, both fundamental building blocks, namely mixed integer linear programming and nonlinear programming, have seen considerable and steady progress in recent years. Wishing to exploit expertise in these areas as well as on previous work in mixed integer nonlinear programming, this

doi:10.1016/j.disopt.2006.10.011
fatcat:cd6osqzvkvhifov4o7t2ekyouu
## more »

... work represents the first step in an ongoing and ambitious project within an open-source environment. COIN-OR is our chosen environment for the development of the optimization software. A class of hybrid algorithms, of which branch and bound and polyhedral outer approximation are the two extreme cases, is proposed and implemented. Computational results that demonstrate the effectiveness of this framework are reported, and a library of mixed integer nonlinear problems that exhibit convex continuous relaxations is made publicly available.##
###
Interior-Point Methods for Estimating Seasonal Parameters in Discrete-Time Infectious Disease Models

2013
*
PLoS ONE
*

t {I tz1 V t[T { ð6Þ I I tz1~b bzaĨ I t zS S t {Ñ N t V t[T { ð7Þ X i[Ty n t(t)~

doi:10.1371/journal.pone.0074208
pmid:24167542
pmcid:PMC3805536
fatcat:ojjh3n3pzfgqroguydbq5x57q4
*D*T y*D*: ð8Þ Here, T y refers to the set of discrete time within a single year, n t(t) is a seasonally varying weight on ... Equation 8 ensures that the number of new susceptibles introduced into the population every year is equal to the number of reported births for each year, and DT y*D*is the cardinality of T y (i.e., the ...##
###
Serotype Diversity and Reassortment between Human and Animal Rotavirus Strains: Implications for Rotavirus Vaccine Programs

2005
*
Journal of Infectious Diseases
*

The development of rotavirus vaccines that are based on heterotypic or serotype-specific immunity has prompted many countries to establish programs to assess the disease burden associated with rotavirus infection and the distribution of rotavirus strains. Strain surveillance helps to determine whether the most prevalent local strains are likely to be covered by the serotype antigens found in current vaccines. After introduction of a vaccine, this surveillance could detect which strains might

doi:10.1086/431499
pmid:16088798
fatcat:kvfdxp34gnbg3ltffgoabcwtcy
## more »

... be covered by the vaccine. Almost 2 decades ago, studies demonstrated that 4 globally common rotavirus serotypes (G1-G4) represent 190% of the rotavirus strains in circulation. Subsequently, these 4 serotypes were used in the development of reassortant vaccines predicated on serotype-specific immunity. More recently, the application of reverse-transcription polymerase chain reaction genotyping, nucleotide sequencing, and antigenic characterization methods has confirmed the importance of the 4 globally common types, but a much greater strain diversity has also been identified (we now recognize strains with at least 42 P-G combinations). These studies also identified globally (G9) or regionally (G5, G8, and P2A[6]) common serotype antigens not covered by the reassortant vaccines that have undergone efficacy trials. The enormous diversity and capacity of human rotaviruses for change suggest that rotavirus vaccines must provide good heterotypic protection to be optimally effective. Globally, rotavirus infection is the most important cause of severe diarrhea in children. Most deaths occur in less-industrialized countries [1], and health organizations worldwide are promoting the development of rotavirus vaccines to help control this disease. The current strategy is based on the use of live, attenuated rotavirus vaccine candidates designed to elicit immunity comparable to that induced by natural rotavirus infections by providing homotypic or heterotypic protection against severe diarrhea caused by the major ro-##
###
The IDAES Process Modeling Framework and Model Library – Flexibility for Process Simulation and Optimization

2021
*
Journal of Advanced Manufacturing and Processing
*

,r X r þ M p:i 8 p, i

doi:10.1002/amp2.10095
fatcat:u2oiunyasrflpovgfvpla2w7w4
*ð*Þ ∈ I Â P Eqn 2 Element (mole basis only) X IÂP p,i*ð*Þ ∂ V ν e,i C p,i À Á ∂t ¼ X IÂP p,i*ð*Þ ν e,i F p,i,in À X IÂP p,i*ð*Þ ν e,i F p,i,out þ M e 8e ∈ E Eqn 3 reaction r. ... r α p,i,r X r,x þ LM p,i,x 8 p,i*ð*Þ ∈ I Â P Eqn 7 Element (mole basis only) L P IÂP p,i*ð*Þ ∂ Aν e,i C p,i,x*ð*Þ ∂t ¼ γν e,i P IÂP p,i*ð*Þ ∂F p,i,x ∂x þ LM e,x 8e ∈ E Eqn 8 Energy Balances Total Enthalpy ...
« Previous

*Showing results 1 — 15 out of 4,259 results*