DCLR与TLA共混改性沥青的性能对比

以SK-90沥青为基质沥青,分别加入相同掺量(与沥青质量比为5%、10%、15%和20%)的DCLR和TLA,利用SHRP PG、针入度分级体系、红外光谱仪和凝胶色谱仪对比分析DCLR和TLA共混改性沥青的宏观性能和微观结构的变化。结果表明,DCLR和TLA的加入均可以改善沥青的高温性能,但对沥青的低温性能和疲劳性能产生一定的不利影响。同时,根据DCLR和TLA共混改性沥青的官能团和分子量分布的变化可以判断DCLR和TLA对沥青的改性作用主要为物理改性,在某种程度上,DCLR不能完全替代TLA。     

A multiprojection algorithm using Bregman projections in a product space

Generalized distances give rise to generalized projections into convex sets. An important question is whether or not one can use within the same projection algorithm different types of such generalized projections. This question has practical consequences in the area of signal detection and image recovery in situations that can be formulated mathematically as a convex feasibility problem. Using an extension of Pierra's product space formalism, we show here that a multiprojection algorithm converges. Our algorithm is fully simultaneous, i.e., it uses in each iterative stepall sets of the convex feasibility problem. Different multiprojection algorithms can be derived from our algorithmic scheme by a judicious choice of the Bregman functions which govern the process. As a by-product of our investigation we also obtain blockiterative schemes for certain kinds of linearly constraned optimization problems.     

多指标试验设计的优化分析

本文给出了多指标试验设计优化分析的归二模型及相应的两种优化方法。并通过一个案例与其它方法进行了比较     

The generalized assignment problem with flexible jobs

The Generalized Assignment Problem (GAP) seeks an allocation of jobs to capacitated resources at minimum total assignment cost, assuming a job cannot be split among multiple resources. We consider a generalization of this broadly applicable problem in which each job must not only be assigned to a resource, but its resource consumption must also be determined within job-specific limits. In this profit-maximizing version of the GAP, a higher degree of resource consumption increases the revenue associated with a job. Our model permits a job’s revenue per unit resource consumption to decrease as a function of total resource consumption, which allows modeling quantity discounts. The objective is then to determine job assignments and resource consumption levels that maximize total profit. We develop a class of heuristic solution methods, and demonstrate the asymptotic optimality of this class of heuristics in a probabilistic sense.     

Feasibility and Stability of a Kind of Model Predictive Control with Additive Uncertainties

In this paper the feasibility and stability of open-loop rain-max model predictive control （OL-MMMPC） for systems with additive bounded uncertainties are considered. It is found that the OL-MMMPC may be infeasible and unstable if it is initially feasible. A sufficient condition for feasibility and stability of the OL- MMMPC is presented. Then an improved OL-MMMPC algorithm is proposed, which guarantees the robust stability of the closed-loop system once it is initially feasible. The effectiveness of this algorithm is illustrated by a simulation example.     

Strict Feasibility Conditions in Nonlinear Complementarity Problems

Strict feasibility plays an important role in the development of the theoryand algorithms of complementarity problems. In this paper, we establishsufficient conditions to ensure strict feasibility of a nonlinearcomplementarity problem. Our analysis method, based on a newly introducedconcept of -exceptional sequence, can be viewed as a unified approachfor proving the existence of a strictly feasible point. Some equivalentconditions of strict feasibility are also developed for certaincomplementarity problems. In particular, we show that aP_*-complementarity problem is strictly feasible if and only ifits solution set is nonempty and bounded.     

A Polynomial Cutting Surfaces Algorithm for the Convex Feasibility Problem Defined by Self-Concordant Inequalities

Consider a nonempty convex set in ^m which is defined by a finite number of smooth convex inequalities and which admits a self-concordant logarithmic barrier. We study the analytic center based column generation algorithm for the problem of finding a feasible point in this set. At each iteration the algorithm computes an approximate analytic center of the set defined by the inequalities generated in the previous iterations. If this approximate analytic center is a solution, then the algorithm terminates; otherwise either an existing inequality is shifted or a new inequality is added into the system. As the number of iterations increases, the set defined by the generated inequalities shrinks and the algorithm eventually finds a solution of the problem. The algorithm can be thought of as an extension of the classical cutting plane method. The difference is that we use analytic centers and convex cuts instead of arbitrary infeasible points and linear cuts. In contrast to the cutting plane method, the algorithm has a polynomial worst case complexity of O(Nlog 1/) on the total number of cuts to be used, where N is the number of convex inequalities in the original problem and is the maximum common slack of the original inequality system.     

Large-scale linear programming techniques for the design of protein folding potentials

We present large-scale optimization techniques to model the energy function that underlies the folding process of proteins. Linear Programming is used to identify parameters in the energy function model, the objective being that the model predict the structure of known proteins correctly. Such trained functions can then be used either for ab-initio prediction or for recognition of unknown structures. In order to obtain good energy models we need to be able to solve dense Linear Programming Problems with tens (possibly hundreds) of millions of constraints in a few hundred parameters, which we achieve by tailoring and parallelizing the interior-point code PCx.     

Random Function Iterations for Consistent Stochastic Feasibility

We study the convergence of iterated random functions for stochastic feasibility in the consistent case (in the sense of Butnariu and Flåm [Numer. Funct. Anal. Optimiz., 1995]) in several different settings, under decreasingly restrictive regularity assumptions of the fixed point mappings. The iterations are Markov chains and, for the purposes of this study, convergence is understood in very restrictive terms. We show that sufficient conditions for geometric (linear) convergence in expectation of stochastic projection algorithms presented in Nedi? [Math. Program, 2011], are in fact necessary for geometric (linear) convergence in expectation more generally of iterated random functions.     

Local analysis of the feasible primal-dual interior-point method

In this paper we analyze the rate of local convergence of the Newton primal-dual interior-point method when the iterates are kept strictly feasible with respect to the inequality constraints. It is shown under the classical conditions that the rate is q-quadratic when the functions associated to the binding inequality constraints are concave. In general, the q-quadratic rate is achieved provided the step in the primal variables does not become asymptotically orthogonal to any of the gradients of the binding inequality constraints.