optimization comparison between cvxpy and gurobi

Question

There are number of jobs to be assigned to number of resources each with a score (performance indicator) and cost. The resource assignment problem (RAP) objective is to maximize assignment scores considering the budget. Constraints: Each resource can handle at most one job and each job if it is filled should be done by one resource. Also, there is a limited budget to spend. I have tackled the problem in two ways: CVXPY using gurobi solver and gurobi packages. My challenge is I can't program it in a memory-efficient way with cvxpy. There are hundreds of constraint list comprehensions! How can I can improve efficiency of my code in cvxpy? For example, is there a better way to define dictionary variables in cvxpy similar to gurobi?

ms is dictionary of format {('firstName lastName', 'job'), score_value}
cst is dictionary of format {('firstName lastName', 'job'), cost_value}
job is set of jobs
res is set of resources {'firstName lastName'}
G (or g in gurobi implementation) is a dictionary with jobs as keys and values of 0 or 1 whether that job is filled due to budget limit (0 if filled and 1 if not)

thanks github link including codes and memory profiling comparison

gurobi implementation:

m = gp.Model("RAP")
assign = m.addVars(ms.keys(), vtype=GRB.BINARY, name="assign")
g = m.addVars(job, name="gap")
m.addConstrs((assign.sum("*", j) + g[j]  == 1 for j in job), name="demand")
m.addConstrs((assign.sum(r, "*") <= 1 for r in res), name="supply")
m.addConstr(assign.prod(cst) <= budget, name="Budget")
job_gap_penalty = 101 # penatly of not filling a job 
m.setObjective(assign.prod(ms) -job_gap_penalty*g.sum(), GRB.MAXIMIZE)
m.optimize()

cvxpy implenentation:

X = {}
for a in ms.keys():
    X[a] = cp.Variable(boolean=True, name="assign")
G = {}
for g in job:
    G[g] = cp.Variable(boolean=True, name="gap")
constraints = []
for j in job:
    X_r = 0
    for r in res:
        X_r += X[r, j]
    constraints += [
        X_r + G[j] == 1
        ]
for r in res:
    X_j = 0
    for j in job:
        X_j += X[r, j]
    constraints += [
        X_j <= 1
    ]
constraints += [
    np.array(list(cst.values())) @ np.array(list(X.values())) <= budget,
]
obj = cp.Maximize(np.array(list(ms.values())) @ np.array(list(X.values()))
                  - job_gap_penalty * cp.sum(list(G.values())))
prob = cp.Problem(obj, constraints)
prob.solve(solver=cp.GUROBI, verbose=False)

Here is the memory profiling comparison:

memeory profiling for cvxpy

memory profiling for gurobi

There is no github link and even if there would be: That's not the type of question this site asks for. Especially in regards to questions completely based on external links (potentially down in the future). In regards to your *basic question*: yes, don't do list-comprehensions, loops and co.; use sparse algebra. Often a slow loop is equivalent to some sparse algebraic expression: e.g. matrix-multiplication with some identity-matrix. Those are what's within the core of cvxpy (and all solvers). — sascha, Jan 09 '21 at 00:53
Thanks for your thoughts @sascha. I have added codes and memory profiling comparison now in question — Mohsen Ghanavati, Jan 09 '21 at 05:11

score 0 · Answer 1 · answered Jan 11 '21 at 20:27

Previously, I tried to solve thru defining dictionary variables similar to gurobi but at is not available in cvxpy, the code was not efficient when scaling up. But now I solved it thru matrix variables and then converting to dictionary variables which super fast!

assign_scores = np.array(list(ms.values())).reshape(len(res), len(job))
assign_cost = np.array(list(cst.values())).reshape(len(res), len(job))

# make a bool matrix variable with the shape of number of resources and jobs
x = cp.Variable(shape=(len(res), len(job)), boolean=True, name="assign")
# make a bool vector variable with the shape of number of jobs
g = cp.Variable(shape=(len(job), ), boolean=True, name="gap")

constraints = []
# each job can be assigned to at most one resource or remains unfilled due to budget cap 
constraints += [cp.sum(x[:, j]) + g[j] == 1 for j in range(len(job))]
# each resource can be assigned to at most one job
constraints += [cp.sum(x[r, :]) <= 1 for r in range(len(res))]
# budget cap
constraints += [cp.sum(cp.multiply(assign_cost, x)) <= budget]

# pentalty if a job is not filled 
job_gap_penalty=101
# objective is to maiximize performance score 
obj = cp.Maximize(cp.sum(cp.multiply(assign_scores, x) - job_gap_penalty * cp.sum(g)))
prob = cp.Problem(obj, constraints)
prob.solve(solver=cp.GUROBI, verbose=True)

optimization comparison between cvxpy and gurobi

1 Answers1