Stanford University Department of Operations Research Systems Optimization Laboratory
Overview
Works:  324 works in 349 publications in 1 language and 469 library holdings 

Roles:  Researcher 
Classifications:  TD353, 333.82 
Publication Timeline
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Most widely held works by
Stanford University
Modeling water supply for the energy sector : final report
by
Nathan Buras(
Book
)
1 edition published in 1982 in English and held by 11 WorldCat member libraries worldwide
1 edition published in 1982 in English and held by 11 WorldCat member libraries worldwide
Determining the feasibility of incorporating water resource constraints into energy models : final report
by
Nathan Buras(
Book
)
2 editions published in 1979 in English and held by 10 WorldCat member libraries worldwide
2 editions published in 1979 in English and held by 10 WorldCat member libraries worldwide
PILOT1980 energyeconomic model
by
George B Dantzig(
Book
)
1 edition published in 1982 in English and held by 10 WorldCat member libraries worldwide
1 edition published in 1982 in English and held by 10 WorldCat member libraries worldwide
Planning under uncertainty solving largescale stochastic linear programs
(
)
2 editions published in 1992 in English and held by 8 WorldCat member libraries worldwide
For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how largescale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving twostage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multicomputer and derive the theory for solving a general class of multistage problems with dependency of the stochastic parameters within a stage and between different stages
2 editions published in 1992 in English and held by 8 WorldCat member libraries worldwide
For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how largescale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving twostage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multicomputer and derive the theory for solving a general class of multistage problems with dependency of the stochastic parameters within a stage and between different stages
Stanford PILOT energy/economic model : interim report
by
George B Dantzig(
Book
)
1 edition published in 1978 in English and held by 5 WorldCat member libraries worldwide
1 edition published in 1978 in English and held by 5 WorldCat member libraries worldwide
Decomposition and (importance) sampling techniques for multistage stochastic linear programs
by
Stanford University(
)
2 editions published in 1993 in English and held by 5 WorldCat member libraries worldwide
The difficulty of solving largescale multistage stochastic linear programs arises from the sheer number of scenarios associated with numerous stochastic parameters. The number of scenarios grows exponentially with the number of stages and problems get easily out of hand even for very moderate numbers of stochastic parameters per stage. Our method combines dual (Benders) decomposition with Monte Carlo sampling techniques. We employ importance sampling to efficiently obtain accurate estimates of both expected future costs and gradients and righthand sides of cuts. The method enables us to solve practical largescale problems with many stages and numerous stochastic parameters per stage. We discuss the theory of sharing and adjusting cuts between different scenarios in a stage. We derive probabilistic lower and upper bounds, where we use importance path sampling for the upper bound estimation. Initial numerical results turned out to be promising
2 editions published in 1993 in English and held by 5 WorldCat member libraries worldwide
The difficulty of solving largescale multistage stochastic linear programs arises from the sheer number of scenarios associated with numerous stochastic parameters. The number of scenarios grows exponentially with the number of stages and problems get easily out of hand even for very moderate numbers of stochastic parameters per stage. Our method combines dual (Benders) decomposition with Monte Carlo sampling techniques. We employ importance sampling to efficiently obtain accurate estimates of both expected future costs and gradients and righthand sides of cuts. The method enables us to solve practical largescale problems with many stages and numerous stochastic parameters per stage. We discuss the theory of sharing and adjusting cuts between different scenarios in a stage. We derive probabilistic lower and upper bounds, where we use importance path sampling for the upper bound estimation. Initial numerical results turned out to be promising
Technical report
by
Stanford University(
Book
)
5 editions published between 1990 and 1991 in English and held by 5 WorldCat member libraries worldwide
5 editions published between 1990 and 1991 in English and held by 5 WorldCat member libraries worldwide
Optimal design of pitched tapered laminated wood beams
by
M Avriel(
Book
)
2 editions published in 1976 in English and held by 4 WorldCat member libraries worldwide
The optimal design of a pitched tapered laminated woood beam is considered. An engineering formulation is given in which the volume of the beam is minimized. The problem is then reformulated and solved as a generalized geometric (signomial) program. Sample designs are presented. (Author)
2 editions published in 1976 in English and held by 4 WorldCat member libraries worldwide
The optimal design of a pitched tapered laminated woood beam is considered. An engineering formulation is given in which the volume of the beam is minimized. The problem is then reformulated and solved as a generalized geometric (signomial) program. Sample designs are presented. (Author)
The simplex algorithm with a new primal and dual pivot rule
(
)
1 edition published in 1993 in English and held by 4 WorldCat member libraries worldwide
We present a simplextype algorithm for linear programming that works with primalfeasible and dualfeasible points associated with bases that differ by only one column
1 edition published in 1993 in English and held by 4 WorldCat member libraries worldwide
We present a simplextype algorithm for linear programming that works with primalfeasible and dualfeasible points associated with bases that differ by only one column
Algorithmic advances in stochastic programming
(
)
1 edition published in 1993 in English and held by 4 WorldCat member libraries worldwide
Practical planning problems with deterministic forecasts of inherently uncertain parameters often yield unsatisfactory solutions. Stochastic programming formulations allow uncertain parameters to be modeled as random variables with known distributions, but the size of the resulting mathematical programs can be formidable. Decompositionbased algorithms take advantage of special structure and provide an attractive approach to such problems. We consider two classes of decompositionbased stochastic programming algorithms. The first type of algorithm addresses problems with a ''manageable'' number of scenarios. The second class incorporates Monte Carlo sampling within a decomposition algorithm. We develop and empirically study an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs within a prespecified tolerance. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of ''realworld'' multistage stochastic hydroelectric scheduling problems. Recently, there has been an increased focus on decompositionbased algorithms that use sampling within the optimization framework. These approaches hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. With this as motivation, we develop a stopping rule theory for algorithms in which bounds on the optimal objective function value are estimated by sampling. Rules are provided for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence interval statements for the quality of the proposed solution can be verified. Issues associated with the application of this theory to two samplingbased algorithms are considered, and preliminary empirical coverage results are presented
1 edition published in 1993 in English and held by 4 WorldCat member libraries worldwide
Practical planning problems with deterministic forecasts of inherently uncertain parameters often yield unsatisfactory solutions. Stochastic programming formulations allow uncertain parameters to be modeled as random variables with known distributions, but the size of the resulting mathematical programs can be formidable. Decompositionbased algorithms take advantage of special structure and provide an attractive approach to such problems. We consider two classes of decompositionbased stochastic programming algorithms. The first type of algorithm addresses problems with a ''manageable'' number of scenarios. The second class incorporates Monte Carlo sampling within a decomposition algorithm. We develop and empirically study an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs within a prespecified tolerance. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of ''realworld'' multistage stochastic hydroelectric scheduling problems. Recently, there has been an increased focus on decompositionbased algorithms that use sampling within the optimization framework. These approaches hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. With this as motivation, we develop a stopping rule theory for algorithms in which bounds on the optimal objective function value are estimated by sampling. Rules are provided for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence interval statements for the quality of the proposed solution can be verified. Issues associated with the application of this theory to two samplingbased algorithms are considered, and preliminary empirical coverage results are presented
Stanford Pilot energy/economic model : interim report, May, 1978
by
George B Dantzig(
Book
)
1 edition published in 1978 in English and held by 4 WorldCat member libraries worldwide
1 edition published in 1978 in English and held by 4 WorldCat member libraries worldwide
Planning under uncertainty using parallel computing
by
George B Dantzig(
Book
)
1 edition published in 1987 in English and held by 4 WorldCat member libraries worldwide
For example, parallel processors may make it possible to come to better grips with the fundamental problems of planning, scheduling, design, and control of complex systems such as the economy, an industrial enterprise, an energy system, a waterresource system, military models for planningandcontrol, decisions about investment, innovation, employment, and healthdelivery systems."
1 edition published in 1987 in English and held by 4 WorldCat member libraries worldwide
For example, parallel processors may make it possible to come to better grips with the fundamental problems of planning, scheduling, design, and control of complex systems such as the economy, an industrial enterprise, an energy system, a waterresource system, military models for planningandcontrol, decisions about investment, innovation, employment, and healthdelivery systems."
On the numerical stability of quasidefinite systems
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)
1 edition published in 1993 in English and held by 4 WorldCat member libraries worldwide
The authors discuss the solution of sparse linear equations Kd = r, where K is a symmetric and specially structured indefinite matrix that often arises in numerical optimization. For such K, the indefinite factorization K = LDL{sup T} is known to exist in exact arithmetic with 1 x 1 pivots and no row or column interchanges. It is shown that the stability of the LDL{sup T} factorization of this matrix is naturally connected with the stability of the LDM{sup T} factorization of a closely related unsymmetric positivedefinite matrix. Conditions are given that allow the stable numerical solution of this system by Gaussian elimination without row and column interchanges
1 edition published in 1993 in English and held by 4 WorldCat member libraries worldwide
The authors discuss the solution of sparse linear equations Kd = r, where K is a symmetric and specially structured indefinite matrix that often arises in numerical optimization. For such K, the indefinite factorization K = LDL{sup T} is known to exist in exact arithmetic with 1 x 1 pivots and no row or column interchanges. It is shown that the stability of the LDL{sup T} factorization of this matrix is naturally connected with the stability of the LDM{sup T} factorization of a closely related unsymmetric positivedefinite matrix. Conditions are given that allow the stable numerical solution of this system by Gaussian elimination without row and column interchanges
Two characterizations of sufficient matrices
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)
1 edition published in 1990 in English and held by 4 WorldCat member libraries worldwide
Two characterizations are given for the class of sufficient matrices defined by Cottle, Pang, and Venkateswaran. The first is a direct translation of the definition into linear programming terms. The second can be thought of as a generalization of a theorem of T.D. Parsons on Pmatrices. 19 refs
1 edition published in 1990 in English and held by 4 WorldCat member libraries worldwide
Two characterizations are given for the class of sufficient matrices defined by Cottle, Pang, and Venkateswaran. The first is a direct translation of the definition into linear programming terms. The second can be thought of as a generalization of a theorem of T.D. Parsons on Pmatrices. 19 refs
Mathematical decomposition techniques for power system expansion planning : final report
(
Book
)
1 edition published in 1988 in English and held by 4 WorldCat member libraries worldwide
1 edition published in 1988 in English and held by 4 WorldCat member libraries worldwide
A discountedcost continuoustime flexible manufacturing and operator scheduling model solved by deconvexification over time
(
)
1 edition published in 1990 in English and held by 4 WorldCat member libraries worldwide
A discountedcost, continuoustime, infinitehorizon version of a flexible manufacturing and operator scheduling model is solved. The solution procedure is to convexify the discrete operatorassignment constraints to obtain a linear program, and then to regain the discreteness and obtain an approximate manufacturing schedule by deconvexification of the solution of the linear program over time. The strong features of the model are the accommodation of linear inequality relations among the manufacturing activities and the discrete manufacturing scheduling, whereas the weak features are intraperiod relaxation of inventory availability constraints, and the absence of inventory costs, setup times, and setup charges
1 edition published in 1990 in English and held by 4 WorldCat member libraries worldwide
A discountedcost, continuoustime, infinitehorizon version of a flexible manufacturing and operator scheduling model is solved. The solution procedure is to convexify the discrete operatorassignment constraints to obtain a linear program, and then to regain the discreteness and obtain an approximate manufacturing schedule by deconvexification of the solution of the linear program over time. The strong features of the model are the accommodation of linear inequality relations among the manufacturing activities and the discrete manufacturing scheduling, whereas the weak features are intraperiod relaxation of inventory availability constraints, and the absence of inventory costs, setup times, and setup charges
MINOS : a largescale nonlinear programming system (for problems with linear constraints) user's guide
by
Bruce A Murtagh(
Book
)
3 editions published in 1977 in English and held by 4 WorldCat member libraries worldwide
3 editions published in 1977 in English and held by 4 WorldCat member libraries worldwide
A strictly improving linear programming alorithm based on a series of Phase 1 problems
(
)
1 edition published in 1992 in English and held by 3 WorldCat member libraries worldwide
When used on degenerate problems, the simplex method often takes a number of degenerate steps at a particular vertex before moving to the next. In theory (although rarely in practice), the simplex method can actually cycle at such a degenerate point. Instead of trying to modify the simplex method to avoid degenerate steps, we have developed a new linear programming algorithm that is completely impervious to degeneracy. This new method solves the Phase II problem of finding an optimal solution by solving a series of Phase I feasibility problems. Strict improvement is attained at each iteration in the Phase I algorithm, and the Phase II sequence of feasibility problems has linear convergence in the number of Phase I problems. When tested on the 30 smallest NETLIB linear programming test problems, the computational results for the new Phase II algorithm were over 15% faster than the simplex method; on some problems, it was almost two times faster, and on one problem it was four times faster
1 edition published in 1992 in English and held by 3 WorldCat member libraries worldwide
When used on degenerate problems, the simplex method often takes a number of degenerate steps at a particular vertex before moving to the next. In theory (although rarely in practice), the simplex method can actually cycle at such a degenerate point. Instead of trying to modify the simplex method to avoid degenerate steps, we have developed a new linear programming algorithm that is completely impervious to degeneracy. This new method solves the Phase II problem of finding an optimal solution by solving a series of Phase I feasibility problems. Strict improvement is attained at each iteration in the Phase I algorithm, and the Phase II sequence of feasibility problems has linear convergence in the number of Phase I problems. When tested on the 30 smallest NETLIB linear programming test problems, the computational results for the new Phase II algorithm were over 15% faster than the simplex method; on some problems, it was almost two times faster, and on one problem it was four times faster
Using MINOS as a subroutine for decomposition
by
Robert Entriken(
Book
)
2 editions published in 1987 in English and held by 3 WorldCat member libraries worldwide
The decomposition algorithm used as an example in this report is nesteddual decomposition. The present implementeation is limited to the conventional serial computers of today, but the future holds great promise for the extension of this work to parallel processors."
2 editions published in 1987 in English and held by 3 WorldCat member libraries worldwide
The decomposition algorithm used as an example in this report is nesteddual decomposition. The present implementeation is limited to the conventional serial computers of today, but the future holds great promise for the extension of this work to parallel processors."
Comparisons of composite simplex algorithms
by
Stanford University(
Book
)
2 editions published in 1987 in English and held by 3 WorldCat member libraries worldwide
The implementations of each algorithm are also discussed. One theme that is present throughout all of the computational experience is that there is no one algorithm which is the best algorithm for all problems."
2 editions published in 1987 in English and held by 3 WorldCat member libraries worldwide
The implementations of each algorithm are also discussed. One theme that is present throughout all of the computational experience is that there is no one algorithm which is the best algorithm for all problems."
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Related Identities
 National Science Foundation (U.S.) Sponsor
 United States Office of Naval Research
 Electric Power Research Institute Sponsor
 United States Department of Energy Sponsor
 Dantzig, George B. (George Bernard) 19142005 Author Editor
 United States Energy Research and Development Administration
 United States Department of Energy Oakland Operations Office Researcher
 United States Department of Energy Office of Scientific and Technical Information Distributor
 Gill, Philip E. Author
 Stanford University Institute for Energy Studies
Associated Subjects
Decomposition (Mathematics) Energy policyEconomic aspects Industrial water supplyMathematical models Laminated wood Linear programming Mathematical optimizationComputer programs Nonlinear programming Nonlinear programmingData processing Parallel processing (Electronic computers) Power resourcesMathematical models Production scheduling Programming (Mathematics) Simplexes (Mathematics) United States Water resources developmentMathematical models WatersupplyMathematical models
Alternative Names
S.O.L.
SOL
Stanford University Department of Operations Research Systems Optimization Laboratory
Stanford University. Dept. of Operations Research. Systems Optimization Laboratory
Stanford University Systems Optimization Laboratory
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