scholarly journals A Generalized Alternating Linearization Bundle Method for Structured Convex Optimization with Inexact First-Order Oracles

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 91 ◽  
Author(s):  
Chunming Tang ◽  
Yanni Li ◽  
Xiaoxia Dong ◽  
Bo He
Keyword(s):  
Optimization Problems ◽  
Bundle Method ◽  
Order Information ◽  
Stochastic Linear Programming ◽  
Proximal Point ◽  
First Order ◽  
Structured Convex Optimization ◽  
Different Types ◽  
Linear Programming Problems ◽  
Structured Optimization

In this paper, we consider a class of structured optimization problems whose objective function is the summation of two convex functions: f and h, which are not necessarily differentiable. We focus particularly on the case where the function f is general and its exact first-order information (function value and subgradient) may be difficult to obtain, while the function h is relatively simple. We propose a generalized alternating linearization bundle method for solving this class of problems, which can handle inexact first-order information of on-demand accuracy. The inexact information can be very general, which covers various oracles, such as inexact, partially inexact and asymptotically exact oracles, and so forth. At each iteration, the algorithm solves two interrelated subproblems: one aims to find the proximal point of the polyhedron model of f plus the linearization of h; the other aims to find the proximal point of the linearization of f plus h. We establish global convergence of the algorithm under different types of inexactness. Finally, some preliminary numerical results on a set of two-stage stochastic linear programming problems show that our method is very encouraging.

scholarly journals Convergence Analysis on an Accelerated Proximal Point Algorithm for Linearly Constrained Optimization Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sha Lu ◽  
Zengxin Wei
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Proximal Point Algorithm ◽  
Original Problem ◽  
Optimization Problems ◽  
Linear Constraints ◽  
Proximal Point ◽  
Constrained Optimization Problems ◽  
Linearly Constrained ◽  
Linear Programming Problems

Proximal point algorithm is a type of method widely used in solving optimization problems and some practical problems such as machine learning in recent years. In this paper, a framework of accelerated proximal point algorithm is presented for convex minimization with linear constraints. The algorithm can be seen as an extension to G u ¨ ler’s methods for unconstrained optimization and linear programming problems. We prove that the sequence generated by the algorithm converges to a KKT solution of the original problem under appropriate conditions with the convergence rate of O 1 / k 2 .

scholarly journals An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Feng Ma ◽  
Mingfang Ni ◽  
Lei Zhu ◽  
Zhanke Yu
Keyword(s):  
Convex Optimization ◽  
Practical Interest ◽  
Principal Component ◽  
Objective Functions ◽  
Dual Algorithm ◽  
Proximal Point ◽  
Pursuit Problem ◽  
First Order ◽  
Structured Convex Optimization ◽  
Primal Dual

Many application problems of practical interest can be posed as structured convex optimization models. In this paper, we study a new first-order primaldual algorithm. The method can be easily implementable, provided that the resolvent operators of the component objective functions are simple to evaluate. We show that the proposed method can be interpreted as a proximal point algorithm with a customized metric proximal parameter. Convergence property is established under the analytic contraction framework. Finally, we verify the efficiency of the algorithm by solving the stable principal component pursuit problem.

scholarly journals LOCALMINIMUM PRINCIPLE FOR OPTIMIZATION PROBLEMS WITH DIFFERENT TYPES OF CONTROL SYSTEMS SUBJECT TO MIXED STATE-CONTROL CONSTRAINTS

2018 ◽  
Vol 14 (2) ◽  
pp. 57-64 ◽  
Author(s):  
Andrey V. Dmitruk ◽  
Nikolay P. Osmolovskii
Keyword(s):  
Control Systems ◽  
Optimal Control Problems ◽  
Optimization Problems ◽  
Fixed Time ◽  
Time Interval ◽  
State Control ◽  
First Order ◽  
Type Integral ◽  
Different Types ◽  
Interval Systems

This paper discusses the first-order optimality conditions for optimal control problems with two different types of control systems, considered on a fixed time interval: systems of ordinary differential equations and systems of Volterra-type integral equations

Post-Non-Classical World View in the On-Screen Reality of the Digital Epoch

2015 ◽  
pp. 4-12
Author(s):  
Elena V. Nikolaeva
Keyword(s):  
World View ◽  
Dynamic Chaos ◽  
Digital Culture ◽  
Fractal Nature ◽  
Late 20Th Century ◽  
First Order ◽  
Classical World ◽  
Different Types ◽  
Strange Loops ◽  
Cultural World

The article analyzes the correlation between the screen reality and the first-order reality in the digital culture. Specific concepts of the scientific paradigm of the late 20th century are considered as constituent principles of the on-screen reality of the digital epoch. The study proves that the post-non-classical cultural world view, emerging from the dynamic “chaos” of informational and semantic rows of TV programs and cinematographic narrations, is of a fractal nature. The article investigates different types of fractality of the TV content and film plots, their inner and outer “strange loops” and artistic interpretations of the “butterfly effect”.

Optimization of Cold-Formed Channel Sections Using Imperialist Competitive Algorithm

2012 ◽  
Vol 166-169 ◽  
pp. 493-496
Author(s):  
Roya Kohandel ◽  
Behzad Abdi ◽  
Poi Ngian Shek ◽  
M.Md. Tahir ◽  
Ahmad Beng Hong Kueh
Keyword(s):  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Computational Method ◽  
Compression Force ◽  
Sqp Method ◽  
Competitive Algorithm ◽  
Different Types ◽  
Formed Channel ◽  
Good Agreement

The Imperialist Competitive Algorithm (ICA) is a novel computational method based on the concept of socio-political motivated strategy, which is usually used to solve different types of optimization problems. This paper presents the optimization of cold-formed channel section subjected to axial compression force utilizing the ICA method. The results are then compared to the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) algorithm for validation purpose. The results obtained from the ICA method is in good agreement with the GA and SQP method in terms of weight but slightly different in the geometry shape.

Quantitative Models of Motor Responses Subject to Longitudinal, Lateral, and Preview Constraints

1978 ◽  
Vol 20 (1) ◽  
pp. 35-39 ◽  
Author(s):  
Tarald O. Kvålseth
Keyword(s):  
Experimental Data ◽  
Motor Control ◽  
Linear Models ◽  
Movement Time ◽  
Arm Movements ◽  
Second Order ◽  
Motor Responses ◽  
First Order ◽  
Different Types ◽  
Index Of Difficulty

First- and second-order linear models of mean movement time for serial arm movements aimed at a target and subject to preview constraints and lateral constraints were formulated as extensions of the so-called Fitts's law of motor control. These models were validated on the basis of experimental data from five subjects and found to explain from 80% to 85% of the variation in movement time in the case of the first-order models and from 93% to 95% of such variation for the second-order models. Fitts's index of difficulty (ID) was generally found to contribute more to the movement time than did either the preview ID or the lateral ID defined. Of the different types of errors, target overshoots occurred far more frequently than undershoots.

scholarly journals Relations between Abs-Normal NLPs and MPCCs. Part 2: Weak Constraint Qualifications

2021 ◽  
Vol Volume 2 (Original research articles>) ◽  
Author(s):  
Lisa C. Hegerhorst-Schultchen ◽  
Christian Kirches ◽  
Marc C. Steinbach
Keyword(s):  
Normal Form ◽  
Optimization Problems ◽  
Constraint Qualifications ◽  
Inequality Constraints ◽  
First Order ◽  
Nonlinear Programs ◽  
Mathematical Programs ◽  
Ongoing Effort ◽  
Equality And Inequality Constraints ◽  
Smooth Optimization

This work continues an ongoing effort to compare non-smooth optimization problems in abs-normal form to Mathematical Programs with Complementarity Constraints (MPCCs). We study general Nonlinear Programs with equality and inequality constraints in abs-normal form, so-called Abs-Normal NLPs, and their relation to equivalent MPCC reformulations. We introduce the concepts of Abadie's and Guignard's kink qualification and prove relations to MPCC-ACQ and MPCC-GCQ for the counterpart MPCC formulations. Due to non-uniqueness of a specific slack reformulation suggested in [10], the relations are non-trivial. It turns out that constraint qualifications of Abadie type are preserved. We also prove the weaker result that equivalence of Guginard's (and Abadie's) constraint qualifications for all branch problems hold, while the question of GCQ preservation remains open. Finally, we introduce M-stationarity and B-stationarity concepts for abs-normal NLPs and prove first order optimality conditions corresponding to MPCC counterpart formulations.

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