Computational study of a family of mixed-integer quadratic programming problems

By D. Bienstock. 1995

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Citations in the corpus

Best Subset Selection via a Modern Optimization Lens (2015, methodology): “Bertsimas and Shioda (2009) proposed a tailored branch-and-bound scheme that can be applied to problem (1.1) using ideas from Furnival and Wilson (1974) and techniques in quadratic optimization, extending and enhancing the proposal of Bienstock (1996).”
Algorithm for cardinality-constrained quadratic optimization (2009, methodology): “To improve on UB 0, we then run the heuristic proposed by [ 4 ].”
Global optimization of mixed-integer nonlinear programs: A theoretical and computational study (2004, background): “Solution strategies explicitly addressing mixed-integer nonlinear programs (MINLPs) have appeared rather sporadically in the literature and initially dealt mostly with problems that are convex when integrality restrictions are dropped and/or contain nonlinearities that are quadratic [24, 22, 16, 23,”
Classification and Regression via Integer Optimization (2007, methodology): “The integer programming models for both classification and regression can be tailored by “implicitly” branching on the integer variables (Bienstock 1996, Bertsimas and Shioda 2004).”
Mixed-integer nonlinear optimization*† (2013, background): “An important financial application of MINLP arises in portfolio optimization (Bienstock, 1996; Jobst et al., 2001).”
Heuristics for cardinality constrained portfolio optimisation (2000, background): “Bienstock [8] [9] considered a similar cardinality constrained portfolio optimisation problem to that formulated above.”
Integer-Programming Software Systems (2005, background): “Effective branching rules that work directly on the continuous variables are given in [12] for this case.”
Algorithms and Software for Convex Mixed Integer Nonlinear Programs (2012, methodology): “Mixed-Integer Nonlinear Programs (MINLP) are optimization problems where some of the variables are constrained to take integer values and the objective function and feasible region of the problem are described by nonlinear functions.”
Convex Models of Distribution System Reconfiguration (2012, methodology): “Furthermore, they can be solved with nearly the same efficiency [8], and thus the model QP represents a practical option for real-time reconfiguration.”
Recent Advances in Mathematical Programming with Semi-continuous Variables and Cardinality Constraint (2013, methodology): “Bienstock [7] proposed to solve the subproblem with original variables and a surrogate constraint ∑n i=1 xi/ui K which replaces the constraint ∑n i=1 yi K .”
Simulated annealing for complex portfolio selection problems (2003, methodology): “This is the case for instance of Dembo et al. [10] (with network flow models), Konno and Yamazaki [15] (with an absolute deviation approach to the measure of risk, embedded in linear programming models), Takehara [24] (with an interior point algorithm) and Bienstock [2] (with a Ôbranch and cutÕ appr”