[PDF] Top 20 Active-set Methods for Submodular Minimization Problems
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Active-set Methods for Submodular Minimization Problems
... using active-set methods in Section ...state-of-art methods and also show an important setting where we show the gain due to the ability to warm-start our ...variation problems to be ... See full document
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Methods for solving constrained convex minimization problems and finding zeros of the sum of two operators in Hilbert spaces
... convex minimization problem (.) is solvable, and let U denote the solution set of ...convex minimization problems, some methods were proposed by some authors (see [] and ... See full document
27
Active learning methods for classification and regression problems
... . In [8], the authors propose a preliminary filtering procedure. A sample is suspect when in its neighbourhood defined by a geometrical graph the portion of examples of the same class is not significantly greater than in ... See full document
141
Regularized gradient projection methods for equilibrium and constrained convex minimization problems
... The set of solutions of ...Numerous problems in physics, optimization and economics reduce to finding a solution of ...Some methods have been proposed to solve the equilibrium problem; see, for ... See full document
22
Strong convergence of projection methods for a countable family of nonexpansive mappings and applications to constrained convex minimization problems
... iterative methods for finding fixed points of nonexpansive map- pings can also be used to solve a convex minimization problem; see, for example, [–] and the references ...quadratic minimization ... See full document
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Active-set prediction for interior point methods using controlled perturbations
... perturbed problems, albeit artificially, our proposal may be remindful of warmstarting techniques for ipms and the related active-set predic- tion techniques that have been developed in that context; ... See full document
38
The forward–backward splitting methods for variational inequalities and minimization problems in Banach spaces
... A set-valued mapping A : X ⇒ X ∗ is said to be a monotone operator if x ∗ –y ∗ , x–y ≥ 0, for all x ∗ ∈ A(x) and for all y ∗ ∈ A(y). It is maximal monotone if its graph is not properly con- tained in the graph of ... See full document
17
Multi step implicit iterative methods with regularization for minimization problems and fixed point problems
... the set of solutions of the equilibrium problem and the set of solutions of the variational inequality problem and the set of fixed points of relatively quasi-nonexpansive mappings in a Banach ... See full document
26
A Submodular Feature Aware Framework for Label Subset Selection in Extreme Classification Problems
... The proposed method was compared with sev- eral state-of-the-art methods with diverse ap- proaches. LEML (Yu et al., 2014), CPLST (Chen and Lin, 2012), CS (Hsu et al., 2009) and SLEEC (Bhatia et al., 2015b) which ... See full document
10
An active set algorithm for a class of linear complementarity problems arising from rigid body dynamics
... Several methods have been developed to solve the LCP. Pivotal methods are the early methods for solving the ...These methods utilize different pivot ...small problems, they do not ... See full document
14
Convex Analysis for Minimizing and Learning Submodular Set Functions
... of submodular functions: on one hand there are very basic or specialized functions that admit simple and practical minimization algorithms, but are fairly limited in what they can describe, and, on the ... See full document
115
Structural and stability investigation of the anticancer drug Cyclophosphamide via quantum chemical calculations :A nanotube drug delivery
... DFT methods, with 6-311G*basis set, were employed toinvestigate the structures, optimization, and energy minimization of Cyclophosphamide ...basis set being summarized in Table ... See full document
11
Iterative algorithms for monotone inclusion problems, fixed point problems and minimization problems
... inequality problems, generalized equilibrium problems, convex minimization problems, and fixed point problems, we can also refer to [–] and the references ...our methods in ... See full document
23
Stochastic Generalized Complementarity Problems in Second-Order Cone: Box-Constrained Minimization Reformulation and Solving Methods
... constrained minimization problems contain an expectation function, we then use SAA method to give approximation ...proximation problems are considered, the conclusions ensure that it is feasible to ... See full document
5
Relaxed iterative algorithms for a system of generalized mixed equilibrium problems and a countable family of totally quasi-Phi-asymptotically nonexpansive multi-valued maps, with applications
... the set of fixed points and asymptotic fixed points of G by F(G) and F(G), respectively. ˆ A subset K of X is said to be a retract of X, if there exists a continuous map P : X → K such that Pu = u, for all u ∈ X. It ... See full document
15
Transformation methods in nonlinear programming
... transformation methods for Problem ...the methods for unconstrained minimization. The best methods currently available are Newton's method and the quasi-Newton methods (see Chapter 5), ... See full document
173
A review of echocardiographic image segmentation techniques for left ventricular study
... Intensity gradient methods utilize abrupt changes in the gray level of pixels at the edges of an object in image. An approach given by Chu et al [18] uses image intensity variation for initial contour extraction. ... See full document
6
Extended Spectral Nonlinear Conjugate Gradien...
... benchmark problems from [1] and compare its numerical performance with that of the other similar method, which include the standard FR conjugate gradient method in ... See full document
8
Greedy Maximization of Functions with Bounded Curvature under Partition Matroid Constraints
... Our second application is the problem of finding the max- imum directed cut of a graph, under partition matroid con- straints. The cut function of a graph is known to be sub- modular and non-monotone in general (Feige, ... See full document
8
Bundle Methods for Regularized Risk Minimization
... The outline of our paper is as follows. In Section 2 we describe BMRM and contrast it with stan- dard bundle methods. We also prove rates of convergence. In Section 3 we discuss implementation issues and present ... See full document
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